Fluorescence imaging-based diagnostic systems have been widely used to diagnose skin diseases due to their ability to provide detailed information related to the molecular composition of the skin compared to conventional RGB imaging. In addition, recent advances in smartphones have made them suitable for application in biomedical imaging, and therefore various smartphone-based optical imaging systems have been developed for mobile healthcare. However, an advanced analysis algorithm is required to improve the diagnosis of skin diseases. Various deep learning-based algorithms have recently been developed for this purpose. However, deep learning-based algorithms using only white-light reflectance RGB images have exhibited limited diagnostic performance. In this study, we developed an auxiliary deep learning network called fluorescence-aided amplifying network (FAA-Net) to diagnose skin diseases using a developed multi-modal smartphone imaging system that offers RGB and fluorescence images. FAA-Net is equipped with a meta-learning-based algorithm to solve problems that may occur due to the insufficient number of images acquired by the developed system. In addition, we devised a new attention-based module that can learn the location of skin diseases by itself and emphasize potential disease regions, and incorporated it into FAA-Net. We conducted a clinical trial in a hospital to evaluate the performance of FAA-Net and to compare various evaluation metrics of our developed model and other state-of-the-art models for the diagnosis of skin diseases using our multi-modal system. Experimental results demonstrated that our developed model exhibited an 8.61% and 9.83% improvement in mean accuracy and area under the curve in classifying skin diseases, respectively, compared with other advanced models.

Extracting manufactured features such as buildings, roads, and water from aerial images is critical for urban planning, traffic management, and industrial development. Recently, convolutional neural networks (CNNs) have become a popular strategy to capture contextual features automatically. In order to train CNNs, a large training data are required, but it is not straightforward to use free-accessible data sets due to imperfect labeling. To address this issue, we make a large scale of data sets using RGB aerial images and convert them to digital maps with location information such as roads, buildings, and water from the metropolitan area of Seoul in South Korea. The numbers of training and test data are 72 400 and 9600, respectively. Based on our self-made data sets, we design a multiobject segmentation system and propose an algorithm that utilizes pyramid pooling layers (PPLs) to improve U-Net. Test results indicate that U-Net with PPLs, called UNetPPL, learn fine-grained classification maps and outperforms other algorithms of fully convolutional network and U-Net, achieving the mean intersection of union (mIOU) of 79.52 and the pixel accuracy of 87.61% for four types of objects (i.e., building, road, water, and background).

This paper presents a modeling of brushless dc motor based servo actuator for manipulators, and described dynamic and fault analysis of the actuator using the model. A new model of brushless dc motors, which have delta windings, is proposed. Nonlinear model of the actuator, which has ball screw and spur gears, is presented, too. Nonlinear modeling of electromechanical actuation systems makes it possible to investigate the effects of faults such as open circuit of motor winding and jamming of gear screw. The simulation using the model examines the effects of nonlinear components on the system performance such as frequency response and dynamic stiffness which is more critical issue in some applications.

This paper described an error analysis of a camera/laser range finder sensor head to characterize the effects of an error of the sensor head components, and a calibration of the sensor head. The sensor head of the telerobotic system, which is being developed for D&D operation of nuclear facilities, improves the work efficiency of a human operator through visual guidance and 3D modeling of the task space. For successful and accurate task executions, three subsystems must be calibrated:1) calibration of the sensor head, 2) calibration of the homogeneous transformation matrix from the sensor head frame to the manipulator frame, and 3) calibration of the manipulator. This paper determined dominant error sources of the sensor head through the analysis of error propagation, and calibrated the sensor head by compensating the dominant error sources

A novel actuator concept has been developed and prototyped. By integrating a multi-speed transmission with a high performance permanent magnet DC servomotor, it has been shown that an electrical drive can provide power/torque density and physical size that approaches the capabilities of electrohydraulics. Preliminary experimental tests of the prototype are encouraging and the first steps toward verifying the concept.

This paper proposes a methodology for the operational fault detection (OFD) of telerobotic systems using discrete wavelet transform and AC analysis. For the generalization of the proposed methodology, the sensor considered in this study is confined to the sensor, which is usually installed in the standard or general configuration of telerobots, such as joint position sensors. Also, the common characteristics and limitations of telerobots are considered in the development of the algorithms.

This paper addresses aspects of the feasibility of extending high performance brushless DC motor torque-speed capabilities by using multi-speed transmissions. Initial dynamic modeling and simulation results show that a transmission-based electrical servo actuator can have similar load capabilities with widely used hydraulic actuator in robot manipulators.

This paper addresses the issues of developing fault tolerant control system for telerobotic systems in operational space. First, the characteristics of operational faults and the relevant sensor signals are studied and classified. Secondly, the framework for FDI and the associated supervisory control scheme are proposed to effectively integrate the FDI approaches into telerobotic systems.

Teleoperation performance can be improved through methods that combine remote operation with robotic techniques and lessen the inefficiencies and complexities inherent in teleoperation. The human machine cooperative telerobotics (HMCT) project uses robotic control techniques, called assistance functions, to enhance the ability of the human operator by modifying the operator's commands. This paper presents the concept of HMCT using the force assistance function based on fuzzy control.