The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial equipment while using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction in order to enhance the accuracy. The one-dimensional convolution neural network (1D-CNN) method can not only diagnose bearing faults accurately, but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. In this paper, the 1D-CNN network architecture is proposed in order to effectively improve the accuracy of the diagnosis of rolling bearing, and the number of convolution kernels decreases with the reduction of the convolution kernel size. The method obtains high accuracy and improves the generalizing ability by introducing the dropout operation. The experimental results show 99.2% of the average accuracy under a single load and 98.83% under different loads.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly and improve travel efficiency. Thus, it is important to manage and schedule the arrival times of buses for the efficient deployment of buses and to ease traffic congestion, which improves the service quality of the public transport system. However, due to many variables disturbing the scheduled transportation, accurate prediction is challenging. For accurate prediction of the arrival time of a bus, this research adopted a recurrent neural network (RNN). For the prediction, the variables affecting the bus arrival time were investigated from the data set containing the route, a driver, weather, and the schedule. Then, a stacked multilayer RNN model was created with the variables that were categorized into four groups. The RNN model with a separate multi-input and spatiotemporal sequence model was applied to the data of the arrival and leaving times of a bus from all of a Shandong Linyi bus route. The result of the model simulation revealed that the convolutional long short-term memory (ConvLSTM) model showed the highest accuracy among the tested models. The propagation of error and the number of prediction steps influenced the prediction accuracy.

Steganography can be viewed as cryptography. It includes a variety of secret communication methods that embed the existence of the message and makes it appear invisible. Both of them have been used to protect secret information. In 2004, Lin and Tsai proposed a novel secret image sharing method that was based on the (k, n)-threshold scheme with the additional capabilities of steganography and authentication. They protect a secret image by breaking it into n user-selected ordinary camouflage images. Besides, their methods also embed fragile watermark signals into the camouflage images by using parity bit checking. Recently, Yang et al. have shown three weaknesses in Lin-Tsai's scheme and further proposed improvements in 2007. They not only improve authentication ability and image quality of stego-image, but also introduce a lossless version image sharing scheme for secret image. Unfortunately, there is a common drawback both in Lin-Tsai's and Yang et al.'s scheme: The enlargement size of the camouflage images and stego-images must be 4 times of the secret image. The proposed scheme does only require 3.5 times enlargement. In addition, the experimental results of image quality analysing show that the proposed scheme has better image quality than previous methods. In other words, the proposed scheme is able to save storage space as well as to improve the image quality

Data hiding technique is an important multimedia security technique and has been applied to many domains, for example, relational databases. The existing data hiding techniques for relational databases cannot restore raw data after hiding. The purpose of this paper is to propose the first reversible hiding technique for the relational database. In hiding phase, it hides confidential messages into a relational database by the LSB (Least-Significant-Bit) matching method for relational databases. In extraction and restoration phases, it gets the confidential messages through the LSB and LSB matching method for relational databases. Finally, the averaging method is used to restore the raw data. According to the experiments, our proposed technique meets data hiding requirements. It not only enables to recover the raw data, but also maintains a high hiding capacity. The complexity of our algorithms shows their efficiencies.