During the age of the Industrial Internet of Things (IIoT), extensive sensors are deployed on steel production lines to construct intelligent monitoring systems. A fan is a crucial piece of machinery in steel production lines, making its fault diagnosis imperative to prevent air pollution and casualties. DNN (Deep Neural Network) with powerful real-time IIoT data analysis has achieved outstanding performance in recognizing faults. Due to the black-box nature of DNNs, these models cannot provide reasonable explanations for their diagnostic decisions. It is still challenging for experts to make reliable and trustworthy conclusions. To address the issue, this paper introduces a new neural-symbolic model, termed Confidence and Classification DBN (CC-DBN), where confidence and classification rules are extracted from a Deep Belief Network (DBN) to provide an explainable representation of DBN feature learning and reasoning. In order to extract confidence rules, this paper develops a new clustering logic Restricted Boltzmann Machine (C-LRBM). Confidence rules can generate latent features of the raw vibration data of the fan and simultaneously explain the hierarchical reasoning of stacked RBM. Besides, to make trustworthy fan diagnosis decisions, classification rules are extracted to provide an explainable symbolic representation between input and output feature spaces. The experiment is performed on an industrial fan dataset from a leading steel production line in Shanghai. The results demonstrate that the proposed CC-DBN can effectively discover knowledge for fan diagnostic decisions and simultaneously achieve superior fault discrimination over typical classifiers and DBNs.

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level of THG images is relatively high, which affects subsequent feature extraction analysis. Denoising THG images is challenging for modern deep-learning based methods because of the rich morphologies contained and the difficulty in obtaining the noise-free counterparts. To address this, in this work, we propose an unsupervised deep-learning network for denoising of THG images which combines a self-supervised blind spot method and a U-shape Transformer using a dynamic sparse attention mechanism. The experimental results on THG images of human glioma tissue show that our approach exhibits superior denoising performance qualitatively and quantitatively compared with previous methods. Our model achieves an improvement of 2.47-9.50 dB in SNR and 0.37-7.40 dB in CNR, compared to six recent state-of-the-art unsupervised learning models including Neighbor2Neighbor, Blind2Unblind, Self2Self+, ZS-N2N, Noise2Info and SDAP. To achieve an objective evaluation of our model, we also validate our model on public datasets including natural and microscopic images, and our model shows a better denoising performance than several recent unsupervised models such as Neighbor2Neighbor, Blind2Unblind and ZS-N2N. In addition, our model is nearly instant in denoising a THG image, which has the potential for real-time applications of THG microscopy.

As an emerging delivery pattern, long-distance non-standardized delivery (LND) is receiving the attention of large logistics companies. Despite the growing demand and customer market for LND, so far there is no mature logistics platform for LND. For logistics companies planning to carry out LND services, there is currently no feasible and effective operations methodology. This paper summarizes the characteristics of LND from the perspective of operations and preliminarily explores the operations methodology of LND. Specifically, the two issues, driver assignment and order settlement for LND, are discussed in detail. To the best of our knowledge, our paper is the first one to study the complete operations methodology for LND. Our proposed methodology could help these large logistics companies that have already launched long-distance standardized delivery services expand the scope of their service. Experimental results demonstrate the feasibility and effectiveness of the proposed driver assignment and order settlement methods. Finally, the paper discusses the impact of drivers’ human factors on LND operations and concludes with several interesting findings. These findings can help the future exploration of how to design a more efficient and beneficial logistics platform for LND.

To respond to the rapid growth of shipping container throughput, terminals urgently need to improve the efficiency of thier operations and reduce operational costs through automation and intellectualization upgrades, thereby improving service levels and enhancing market competitiveness. Due to the advantages of reliable transportation, efficient operation, and environmental friendliness, AGV-based automated container terminal (ACT) has become the development trend of container terminals. To help ACT improve its operational management capabilities, plenty of scholars have explored the transportation system of ACT. Through the analysis of operational management issues, the paper defines the four main research topics in vehicle transportation of the ACT including equipment scheduling, path planning, exception handling, and vehicle management. Then, in each topic, the works in the recent 25 years are summarized and several research opportunities for possible follow-up research directions in different fields are proposed. We expect our survey could not only provide references for more scholars on the research of operation and management of terminals, but also provide guidance for system evaluation and improvement for terminal system engineers and operation managers.

Emission control area has been set up in many countries to reduce the environmental impact of vessels' emissions. However, the regulations for controlling emissions are frequently violated due to the cost of high-quality fuel. Drones currently have become an accurate and efficient way to monitor the vessels' emissions, which should be properly scheduled to cover more and higher risk of violations when facing a large number of vessels. In this paper, a scheduling model is proposed to simulate the drone scheduling monitoring problem. Due to the movement of vessels over time, the complexity of the model is too large to be solved by classical optimization methods such as CPLEX. An ant colony system algorithm is proposed to solve the scheduling problem of drones. Our method is proved to be more effective and efficient when facing a large number of vessels and drone stations in numerical experiments.

This paper discussed the contradictory between wait time for computation and solution quality in solving flexible job shop scheduling problems. In order to reconcile this contradictory, a novel scheduling strategy called Simultaneous Scheduling is proposed. At first, a fairly good solution is obtained in a short time and the solution is set as the temporary processing plan so that the machines can start to work as soon as possible. Then while the machines are running, the temporary plan is being improved with evolutionary algorithms continuously. Experiments on both static and dynamic scheduling problems are performed. The results show that simultaneous scheduling is effective and efficient.

The traditional fault diagnosis methods for rolling bearing usually require the test data and training data to follow the same distribution, which cannot be always meet in real-world scenarios, since the working condition of rolling bearing is often variable. Hence, to overcome the low performance of fault diagnosis traditional methods for different data distributions, a fault diagnosis approach based on transfer learning is proposed in this paper. And the main idea of our approach is to combine joint distribution adaptation and support vector machine to diagnose bearing faults under variable working conditions. In this research, kernel-JDA is used to reduce the difference between distributions of datasets taking both the marginal and conditional distributions into consideration, while the parameters of kernel-JDA are optimized to improve the performance. Besides, multi-features including time domain features and the relative wavelet packet energy are constructed at first to prepare for fault diagnosis. After mapping the multi-features through kernel-JDA, SVM is utilized to diagnose faults of rolling bearing under different working conditions. In addition, comparison experiments on vibration signal datasets of rolling bearings are carried out to verify the effectiveness and applicability of this approach for both the normal and small sizes of the sample sets.

This paper presents an original experimental protocol, which aims to study human reliability in railway systems by computing the human error probability (HEP) of human operators. The experiment is conducted on a railway traffic management system that places operators in simulated situations involving railway failures. The obtained experimental result is analyzed first by two classical human reliability analysis methods to estimate the HEP of each subject. Then, a model of human operators using valuation-based system is proposed. Finally, a methodology automatically populates the proposed model by allowing the verification of temporal properties on the simulation trace.

Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.e., hollowed meshes). In this paper, we present a skeleton-based algorithm for partitioning a 3D surface model into the least number of parts for 3D printing without using any support structure. To achieve support-free fabrication while minimizing the effect of the seams and cracks that are inevitably induced by the partition, which affect the aesthetics and strength of the final assembled surface, we put forward an optimization system with the minimization of the number of partitions and the total length of the cuts, under the constraints of support-free printing angle. Our approach is particularly tailored for shell models, and it can be applicable to solid models as well. We first rigorously show that the optimization problem is NP-hard and then propose a stochastic method to find an optimal solution to the objectives. We propose a polynomial-time algorithm for a special case when the skeleton graph satisfies the requirement that the number of partitioned parts and the degree of each node are bounded by a small constant. We evaluate our partition method on a number of 3D models and validate our method by 3D printing experiments.