Journal of Intelligent and Fuzzy Systems

Xu, Wenxiang; Wang, Lei; Liu, Dezheng; Tang, Hongtao; Li, Yibing

doi: 10.3233/jifs-223435pmid: N/A

Multi-agent collaborative manufacturing, high energy consumption and pollution, and frequent operation outsourcing are the three main characteristics of large complex equipment manufacturing enterprises. Therefore, the production scheduling problem of large complex equipment to be studied is a distributed flexible job shop scheduling problem involving operation outsourcing (Oos-DFJSP). Besides, the influences of each machine on carbon emission and job scheduling at different processing speeds are also involved in this research. Thus the Oos-DFJSP of large complex equipment consists of the following four sub-problems: determining the sequence of operations, assigning jobs to manufactories, assigning operations to machines and determining the processing speed of each machine. In the Oos-DFJSP, if a job is assigned to a manufactory of a group manufacturing enterprise, and the manufactory cannot complete some operations of the workpiece, then these operations will be assigned to other manufactories with related processing capabilities. Aiming at solving the problem, a multi-objective mathematical model including costs, makespan and carbon emission was established, in which energy consumption, power generation of waste heat and treatment capacity of pollutants were considered in the calculation of carbon emission. Then, a multi-objective improved hybrid genetic artificial bee colony algorithm was developed to address the above model. Finally, 45 groups of random comparison experiments were presented. Results indicate that the developed algorithm performs better than other multi-objective algorithms involved in the comparison experiments not only on quality of non-dominated solutions but also on Inverse Generational Distance and Error Ratio. That is, the proposed mathematical model and algorithm were proved to be an excellent method for solving the multi-objective Oos-DFJSP.

Zhang, Shirong; Zhao, Lihong

doi: 10.3233/jifs-224334pmid: N/A

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

Li, Weidong; Yu, Yongbo; Meng, Fanqian; Duan, Jinlong; Zhang, Xuehai

doi: 10.3233/jifs-230041pmid: N/A

Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees).

Hymlin Rose, S.G.; Jayasree, T.

doi: 10.3233/jifs-220443pmid: N/A

A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches.

Karabacak, Mesut

doi: 10.3233/jifs-222625pmid: N/A

The correlation coefficient (CC) is a well-known functional information measures used to measure the interrelationship between uncertain, fuzzy sets. The use of neutrosophic sets (NS) in decision making has been increasing in recent times. Many studies have been considered to calculate the CC of NSs. These approaches assess only the strength of relationship between PNSs, and are described within the interval [0, 1]. However, the inclusion of the reliability level of the data in the process is very important for the final decision. Therefore, neutrosophic Z-Number sets (NZNS) has been defined for this purpose, which are not only provide an assessment of the data but also take into account their confidence level. In this study, we define a correlation coefficient for NZNSs (CCNZNS) by employing the notions of mean, variance and covariance, and discuss some of its properties. This new approach defines correlation in the interval [–1, 1] similar to classical statistics, and indicates whether the NZNSs are either positively or negatively correlated. Then, two decision models are developed for the NZNS universe. In order to determine the partial known attribute weights, a maximizing optimization technique is derived which is taking into account both the objective and subjective aspects of assessments. To demonstrate the effectiveness of the proposed models, the first model is applied for solving a medical diagnostic problem. Then the performance evaluation process is chosen to demonstrate the application of the second model. Finally, the superior aspects of the developed models over other existing models are presented with a comparison and discussion analysis. The study is concluded with the conclusion part.

Liu, Wei; Liu, Qihan; Ye, Guoju; Zhao, Dafang; Guo, Yating; Shi, Fangfang

doi: 10.3233/jifs-222781pmid: N/A

The interval rough number rough sets model is the generalization of the classical rough sets. Since the lower approximation condition of interval rough number rough sets model is a full inclusion relation which is too strict to tolerate noisy data, strict conditions increase the possibility of a sample classified into a wrong class. To overcome the above shortcomings, an interval rough number variable precision rough sets model is proposed in this paper, which is combined with interval rough number similarity and the concept of variable precision rough sets. The model introduces the error parameter and can improve the tolerance of noise data. Then the related properties of the model are also proved. Moreover, we construct a maximal positive domain attribute reduction method based on the proposed model, which can process the data type of interval rough number without discretization. Finally, numerical examples are given to verify the rationality of the model.

Tran, Van Quan

doi: 10.3233/jifs-222899pmid: N/A

The unconfined compressive strength (Qu) is one of the most important criteria of stabilized soil to design in order to evaluate the effective of soft soil improvement. The unconfined compressive strength of stabilized soil is strongly affected by numerous factors such as the soil properties, the binder content, etc. Machine Learning (ML) approach can take into account these factors to predict the unconfined compressive strength (Qu) with high performance and reliability. The aim of this paper is to select a single ML model to design Qu of stabilized soil containing some chemical stabilizer agents such as lime, cement and bitumen. In order to build the single ML model, a database is created based on the literature investigation. The database contains 200 data samples, 12 input variables (Liquid limit, Plastic limit, Plasticity index, Linear shrinkage, Clay content, Sand content, Gravel content, Optimum water content, Density of stabilized soil, Lime content, Cement content, Bitumen content) and the output variable Qu. The performance and reliability of ML model are evaluated by the popular validation technique Monte Carlo simulation with aided of three criteria metrics including coefficient of determination R2, Root Mean Square Error (RMSE) and Mean Square Error (MAE). ML model based on Gradient Boosting algorithm is selected as highest performance and highest reliability ML model for designing Qu of stabilized soil. Explanation of feature effects on the unconfined compressive strength Qu of stabilized soil is carried out by Permutation importance, Partial Dependence Plot (PDP 2D) in two dimensions and SHapley Additive exPlanations (SHAP) local value. The ML model proposed in this investigation is single and useful for professional engineers with using the mapping Maximal dry density-Linear shrinkage created by PDP 2D.

Hou, Yan-e; Wang, Chunxiao; Wang, Congran ; Fan, Gaojuan

doi: 10.3233/jifs-223404pmid: N/A

Multi-compartment vehicle routing problem (MCVRP) is an extension of the classical capacitated vehicle routing problem where products with different characteristics are transported together in one vehicle with multiple compartments. This paper deals with this problem, whose objective is to minimize the total travel distance while satisfying the capacity and maximum route length constraints. We proposed a hybrid iterated local search metaheuristic (HILS) algorithm to solve it. In the framework of iterated local search, the current solution was improved iteratively by five neighborhood operators. For every obtained neighborhood solution after the local search procedure, a large neighborhood search-based perturbation method was executed to explore larger solution space and get a better neighborhood solution to take part in the next iteration. In addition, the worse solutions found by the algorithm were accepted by the nondeterministic simulated annealing-based acceptance rule to keep the diversification of solutions. Computation experiments were conducted on 28 benchmark instances and the experimental results demonstrate that our presented algorithm finds 17 new best solutions, which significantly outperforms the existing state-of-the-art MCVRP methods.

Lei, Deming; Du, Haoyang; Tang, Hongtao

doi: 10.3233/jifs-223929pmid: N/A

Distributed assembly flow shop scheduling problem (DAFSP) has been extensively considered; however, DAFSP with Pm → 1 layout, in which m parallel machines are at fabrication stage and one machine is at assembly stage, is seldom handled. In this study, DAFSP with the above layout and transportation time is studied and an imperialist competitive algorithm with cooperation and division (CDICA) is presented to minimize makespan. Feature of the problem is used and a heuristic is applied to produce initial solution. Adaptive assimilation and evolution are executed in the weakest empire and adaptive cooperation is implemented between the winning empire and the weakest empire in imperialist competition process. Empire division is performed when a given condition is met. Many experiments are conducted. The computational results demonstrate that new strategies are effective and CDICA is a very competitive in solving the considered DAFSP.

Wang, Ning; Zhu, Ping

doi: 10.3233/jifs-224215pmid: N/A

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.