Robustness of neural network models is important in fault diagnosis (FD) because uncertainty in operating conditions varies the power spectral densities of vibration data; however, it is unknown to users due to the limited explainability of the models. This article proposes an FD framework with a power-perturbation-based decision boundary analysis (POBA) to explain the decision boundaries of vibration classification models. In the POBA, perturbed data are obtained from training data by power perturbation on frequency bands centering on dominant class-discriminative frequencies. The decision boundary of a model is then evaluated and visualized to users by testing the model on the perturbed data. Furthermore, the decision boundary information can be used to define a robustness score per class, and a robust model can be obtained by ensembling trained models using their robustness score per class. Demonstration using two vibration datasets verifies the explainability and robustness of the proposed FD framework.

This article proposes a new end-to-end deep learning model for fault diagnosis using three-axis vibration signals measured from facilities. The three-axis vibration signals measured in the time domain are used without domain transformations to train the end-to-end model. The proposed model is designed to effectively extract feature maps of eachX-,Y-, andZ-axis individually from the three-axis vibration signals using a grouped 1-D convolution. The feature maps extracted from each axis are composed of specific frequencies of each axis. Accordingly, the proposed model classifies faults based on the frequency characteristics of each axis. In addition, this article proposes a method to visualize the decision criteria of the proposed model in the frequency domain. Using the proposed model and the proposed visualization method, it is possible to grasp the degree to which specific frequencies of eachX-,Y-, andZ-axis affect the decision criteria of the proposed model. The experiment is conducted by applying the proposed model to an open dataset with three-axis vibration signals measured from a rotary machine. Experimental results demonstrate that the proposed model achieves high accuracy and provides the explainability in eachX-,Y-, andZ-axis using the visualized decision criteria in the frequency domain.

A linear motion (LM) guide is a mechanical tool for requiring linear motion in a system. Repeating linear movements can cause cracking and deterioration of the LM guide, which can lead to a decrease in productivity. Therefore, predicting the status of the LM guide and diagnosing faults are essential for systems including the LM guide. In this article, we propose a novel framework of fault diagnosis model based on deep learning using a vibration sensor signal mounted on the LM guide. This framework contains the learning vibration signal in the time domain using the proposed 1-D convolutional neural network model and the visualization of the classification criteria in the frequency domain using the learned model in the time domain. To utilize the visualization in the frequency domain, the proposed model is designed to maintain the frequency information in the learning process. With the learned model, we propose a frequency domain-based grad-CAM to visualize the classification criteria in the frequency domain to help to explain the characteristics of normal and fault data. Using LM guide data under various conditions, we visualize the classification criteria of the learned model in the frequency domain.

State-of-the-art silicon photonics technology has demonstrated its potential use in all required building blocks for ultrahigh bandwidth on-chip optical links. However, a robust system-level abstraction model reflecting the properties of optical devices has not been well established. We propose a linear optical device model (LODM) for silicon photonic devices and an associated computation method of optical signal propagation (CMOP) in an optical network-on-chip (ONoC). The CMOP manipulates the optical signal routing paths according to the topology, router configuration, and routing algorithm of the given ONoC architecture; thus, it allows the transformed information to be adaptable in an LODM to facilitate simplified analysis. Furthermore, we construct a linear system model of a microring resonator (MR) to reduce the computation complexities caused by its resonance structure. By using the CMOP, we accelerate the system-level analysis of optical signal propagation in ONoCs, reflecting the propagation loss, interference, and phase shift with close accuracy to analog and mixed-signal extensions (AMS) environments. The evaluation results show that the computation speeds up by three orders of magnitude with 1.57% error in accuracy when compared to the AMS environments.

This paper aims to develop a system to guide the evacuation route based on augmented reality (AR) through mobile application in order to enable people to evacuate even more quickly in situation where there is not much smoke after the fire. At present, there are some mobile applications based on location-based services (LBS). Among them, `Pokemon Go' is a mobile application game, which utilizes LBS with global positioning system (GPS). In this paper, the AR-based evacuation route guidance system is proposed based on conventional Convolution Neural Network (CNN) using CCTV image processing in indoor fire environment. In addition, a simple experiment is executed, and the performance of fire recognition and AR-based evacuation guidance are measured.

The rate of donations made by individuals is relatively low in Korea when compared to other developed countries. To address this problem, we propose the DONA, an urban donation motivating robot prototype. The robot roams around in a public space and solicits donation from passers-by by engaging them through a pet like interaction. In this paper, we present the prototype of the robot and our design process.

The rate of donations made by individuals is relatively low in Korea when compared to other developed countries. To address this problem, we propose the DONA, an urban donation motivating robot prototype. The robot roams around in a public space and solicits donation from passers-by by engaging them through a pet like interaction. In this paper, we present the prototype of the robot and our design process.