Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Yang, Yanli | Li, Chenxia

Article Type: Research Article

Abstract: Generalization ability is known as an important performance index of artificial neural networks (ANNs). The generalization ability of an ANN usually refers to its ability to recognize untrained samples, but it lacks quantitative analysis. A method is designed by using frequency-domain signals to observe the generalization ability of deep feedforward neural networks (DFFNNs) which are popular ANN models. This method allows us to observe that the generalization ability of DFFNNs is limited to a small neighborhood around the trained samples. Then, the relationship between sample similarity and the DFFNN’s generalization performance is further analyzed. The analysis results show that the …correlation coefficient between samples has a certain positive correlation with the DFFNN’s generalization performance. Based on this new understanding, an algorithm in which shadows of the trained samples are added into the training set is proposed to improve the generalization ability of DFFNNs. The proposed algorithm is tested with some simulated signals and some real-world data. The tests show that the proposed method can indeed improve the DFFNN’s generalization ability by only changing the training sample set. Show more

Keywords: Deep feedforward neural network, deep learning, artificial neural network, generalization ability, correlation coefficient

DOI: 10.3233/JIFS-201679

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Shu, Yufeng | Zhang, Liangchao | Zuo, Dali | Zhang, Junhua | Li, Junlong | Gan, Haoquan

Article Type: Research Article

Abstract: If the appearance of an eco-friendly textile fabric is problematic, the product quality will be substantially deteriorated. Defect measurement is one of the most important quality control measures for eco-friendly textile fabrics. Compared to previously employed manual measurements, the application of image processing technology for the detection of eco-friendly textile defects is characterized by high efficiency and high precision. In this study, the main objectives of textile reinforcement based on texture enhancement are as follows:(1) Summarize the description methods of texture maps in a certain space and a certain frequency and investigate the gray-scale co-occurrence matrix of textile fabrics, which …aimed at the characteristics of a unique texture of textile fabrics, the texture of the background caused by noise, and the texture of the defect area. The error between them was analyzed; (2) Apply a scheme based on principal component analysis-non local means to improve the eco-friendly textile quality. The image information used in the calculation process of the neighborhood similarity in nonlocal average filtering algorithm (NLM) includes the problem of an excess amount due to noise, and the NLM method is employed to estimate the parameters. On the other hand, to remove the noise, it is also possible to display the texture image content of the textile fabric, which is more conducive to the defect detection; and (3) Apply a texture-based textile defect measurement method, that is, a class-separable characteristic between non-defective and defect textures, which increases the measurement of the gray matrix characteristics that distinguish the defect regions and improves the correctness of the detected texture. Summarize the description methods of texture maps in a certain space and a certain frequency and investigate the gray-scale co-occurrence matrix of textile fabrics, which aimed at the characteristics of a unique texture of textile fabrics, the texture of the background caused by noise, and the texture of the defect area. The error between them was analyzed; Apply a scheme based on principal component analysis-non local means to improve the eco-friendly textile quality. The image information used in the calculation process of the neighborhood similarity in nonlocal average filtering algorithm (NLM) includes the problem of an excess amount due to noise, and the NLM method is employed to estimate the parameters. On the other hand, to remove the noise, it is also possible to display the texture image content of the textile fabric, which is more conducive to the defect detection; and Apply a texture-based textile defect measurement method, that is, a class-separable characteristic between non-defective and defect textures, which increases the measurement of the gray matrix characteristics that distinguish the defect regions and improves the correctness of the detected texture. Show more

Keywords: Texture enhancement, cloth, eco-friendly textile defect, detection method

DOI: 10.3233/JIFS-189704

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Xie, Yi | Wang, Yulin | Ma, Maode

Article Type: Research Article

Abstract: Today, the manner in which we communicate has greatly advanced. The technology is not just about machines, but people with technology together. Machine-to-machine (M2M) communication is unavoidable in the Internet of things. However, at the same time, there are more attacks against the M2M system. Therefore, a reliable and secure authentication mechanism is required. Blockchain technology is decentralized and highly secure while being tamper-proof. This protects M2M service providers by eliminating the single point of failures. This paper proposes a blockchain-based authentication scheme that uses a practical Byzantine fault tolerance (pBFT) consensus mechanism for M2M security in cyber physical systems. …By implementing a blockchain to an M2M system, it provides an ID for devices on the blockchain. Simulation results have shown that the data on the chain cannot be altered. A pBFT consensus algorithm also ensures that the blockchain network is able to come to a consensus with faults. Show more

Keywords: Machine-to-machine communication, blockchain, pBFT consensus algorithm

DOI: 10.3233/JIFS-189702

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2021

Authors: Pei, Cong | Jiang, Feng | Li, Mao

Article Type: Research Article

Abstract: With the advent of cost-efficient depth cameras, many effective feature descriptors have been proposed for action recognition from depth sequences. However, most of them are based on single feature and thus unable to extract the action information comprehensively, e.g., some kinds of feature descriptors can represent the area where the motion occurs while they lack the ability of describing the order in which the action is performed. In this paper, a new feature representation scheme combining different feature descriptors is proposed to capture various aspects of action cues simultaneously. First of all, a depth sequence is divided into a series …of sub-sequences using motion energy based spatial-temporal pyramid. For each sub-sequence, on the one hand, the depth motion maps (DMMs) based completed local binary pattern (CLBP) descriptors are calculated through a patch-based strategy. On the other hand, each sub-sequence is partitioned into spatial grids and the polynormals descriptors are obtained for each of the grid sequences. Then, the sparse representation vectors of the DMMs based CLBP and the polynormals are calculated separately. After pooling, the ultimate representation vector of the sample is generated as the input of the classifier. Finally, two different fusion strategies are applied to conduct fusion. Through extensive experiments on two benchmark datasets, the performance of the proposed method is proved better than that of each single feature based recognition method. Show more

Keywords: Action recognition, feature fusion, depth motion maps, completed local binary pattern, polynormal

DOI: 10.3233/JIFS-200954

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Diao, Lijuan | Song, Shoujun | Cao, Gaofang | Kong, Yang

Article Type: Research Article

Abstract: Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation …space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction. Show more

Keywords: Temporal knowledge graph, knowledge graph link prediction, translation model TransR, long short term memory (LSTM) networks, incremental learning, deep learning

DOI: 10.3233/JIFS-189687

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhang, Qiang | Zhang, Jiayao | Tian, Ying

Article Type: Research Article

Abstract: The performance of the pick gradually degrades under the cyclic complex load, so it is difficult to realize the state prediction, in order to be able to accurately predict the pick degradation state, this paper develops a test system for the wear degradation of pick, the method of experimental analysis and numerical simulation is adopted, Multi-signal fusion model of vibration and acoustic emission was constructed, the grey prediction and the Gamma method of Bayesian parameter updating are used to realize the application of the degenerate data, the results prove that the relative error of grey prediction under vibration signal is …only 0.45%, the relative error of the Gamma model was 0.57%, the relative error of the Gamma model under Bayesian updating was 0.22%, the three models have good prediction accuracy, the prediction error of the Gamma model under Bayesian updating is minimal. Show more

Keywords: Pick wear, pick degradation, grey prediction, gamma process

DOI: 10.3233/JIFS-189684

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Tang, Fei

Article Type: Research Article

Abstract: To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current …generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm. Show more

Keywords: Individual coding, branch population, genetic algorithm, tree growth algorithm

DOI: 10.3233/JIFS-190487

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Tang, Yongwei | Hao, Huijuan | Zhou, Jun | Lin, Yuexiang | Dong, Zhenzhen

Article Type: Research Article

Abstract: AGV (Automated Guided Vehicle) technology has attracted increasing attention. Precise control of AGV position and attitude information in complex operating environment is a key part of smart factories. With outdoor AGV as a platform, this study uses BDS/INS combined navigation system combining Beidou positioning system and inertial navigation system and takes the velocity and position difference between BDS and INS as a model. An integrated navigation method is proposed to improve bee colony algorithm and optimize the BP neural network-assisted Kalman filtering to achieve accurate positioning. Moreover, the optimization of BP neural network navigation using INS navigation, network-assisted navigation and …bee colony algorithm is simulated. Results demonstrate that the integrated navigation algorithm has effectiveness and feasibility, and can solve the problems of BDS misalignment and large INS navigation error in complex environments. Show more

Keywords: AGV, BDS/INS integrated navigation, Kalman filter, neural network, bee colony algorithm

DOI: 10.3233/JIFS-189690

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Sha, Xiuyan | Yina, Chuancun | Xu, Zeshui | Zhang, Shen

Article Type: Research Article

Abstract: In order to fully consider the decision-maker’s limited rationality and attitude to risk, this paper constructs the probabilistic hesitant fuzzy TOPSIS emergency decision-making model based on the cumulative prospect theory under the probabilistic hesitant fuzzy environment. Aiming at the problem of missing probabilistic information in the probabilistic hesitant fuzzy element, a new complement scheme is proposed. In this scheme, the weighted average result of the original data information is used to complement, and the original data information is retained to a large extent. Then this paper proposes several probabilistic hesitant fuzzy distance measures based on Lance distance. The decision reference …point is constructed by the probabilistic hesitant fuzzy Lance distance, which overcomes the influence of the extreme value on the decision-making result, and defines the value function based on the probability hesitation fuzzy Lance distance. In view of the fact that the attribute weights are completely unknown, the probabilistic hesitant fuzzy exponential entropy is constructed by using the actual data, and the attribute weights of different prospect states are obtained. Aiming at the problem that attribute weights of different prospect states have different effects on the cumulative prospect value, the expression of the cumulative prospect value is improved. The improved closeness coefficient of the TOPSIS method is used to order the emergency schemes. Finally, the new method is applied to the emergency decision-making case of a sudden outbreak of epidemic respiratory disease. The results show that the contrast of the new method is obvious, which is conducive to distinguish different schemes. The new method is more suitable for the complex and changeable emergency decision-making field. Show more

Keywords: Probabilistic hesitant fuzzy Lance distance, Probabilistic hesitant fuzzy exponential entropy, Cumulative prospect theory, TOPSIS

DOI: 10.3233/JIFS-201119

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Zhang, Zhaojun | Li, Xuanyu | Luan, Shengyang | Xu, Zhaoxiong

Article Type: Research Article

Abstract: Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. …Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the particle swarm optimization algorithm, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework. Show more

Keywords: Particle swarm optimization, homotopy analysis method, initial population, t-test

DOI: 10.3233/JIFS-200979

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021