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Developing an intelligent robot platform that has multiple motion modes and can actively adjust the motion mode according to an operation situation is necessary in resolving the inability of single-mode robots to adapt completely to complex environments. In this study, we designed a robot that has multiple motion modes and can switch freely in these existing modes. The robot has the following modes: Four-legged walking, two-legged walking, pulse scrolling, and crawler movement. In the robot’s design, these modes are not just simple mechanisms involving superposition and motion switching. These modes involve a degree of freedom in reuse, thereby reducing the number of drives and obtaining a compact structure while achieving a coherent transition of each action mode. For higher load ratios, motion continuity are increased. The robot platform can balance itself during movement by adjusting its own mechanism or providing additional force and change its shape and posture over some obstacles by adjusting its own mechanism.

"Making 3D deep neural networks debuggable". In the study, we develop and propose a 3D deep neural network visualization methodology for performance evaluation of 3D deep neural networks. Our research was conducted using a 3D deep neural network model, which shows the best performance. The visualization method of the research is a method of visualizing part of the 3D object by analyzing the naive Bayesian 3D complement instance generation method and the prediction difference of each feature. The method emphasizes the influence of the network in the process of making decisions. The result of visualization through the algorithm of the study shows a clear difference based on the result class and the instance within the class, and the authors can obtain insight that can evaluate and improve the performance of the DNN (deep neural networks) model by the analyzed results. 3D deep neural networks can be made "indirectly debuggable", and after the completion of the visualization method and the analysis of the result, the method can be used as the evaluation method of "general non-debuggable DNN" and as a debugging method.

Many common path optimization algorithms are available. However, problems arise when a general route optimization algorithm is applied to land transportation of large cargo, such as a modular plant. The large and heavy structure of a modular plant can lead to a loss of time depending on the curve of the road. This problem is more critical when traveling through large turns, which may also cause mechanical problems. Therefore, curves are essential parameters for modular plant land transportation. In this research, we show the importance of angles in the path via multi-body dynamic simulations and finite element analysis. Based on these results, we constructed a pathfinding algorithm that considers the importance of angles. The traditional A-star algorithm considers only distance as a cost, whereas our modified A-star algorithm considers both distance and angle as costs. Our goal is to improve traditional A-star algorithms and optimize them for modular plant land transportation.

Recently, the robots market is growing rapidly, and robots are being applied in various industrial fields. In the future, robots will work in more complex and diverse environments. For example, a robot can perform one or more tasks and collaborate with people or other robots. In this situation, the path planning for the robots to perform their tasks efficiently is an important issue. In this study, we assume that the mobile robot performs one or more tasks, moves various places freely, and works with other robots. In this situation, if the path of the mobile robot is planned with the shortest path algorithm, waiting time may occur because the planned path is blocked by other robots. Sometimes it is possible to complete a task in a shorter time than returning or performing another task first. That is, the shortest path and the shortest path do not coincide with each other. The purpose of this study is to construct a network in which the mobile robot designs the shortest path planning considering shortest time by judging itself based on environment information and path planning information of other robots. For this purpose, a network is constructed using a recurrent neural network and reinforcement learning is used. We established the environment for network learning using the robot simulation program, V-Rep. We compare the effects of various network structures and select network models that meet the purpose. In the future work, we will try to prove the effect of network by comparing existing algorithm and network.

In this study, we develop a transportability evaluation system for the transportation process or design process in product development engineering. The system extracts the constraint condition and transportability using swept path analysis and multi-body dynamic simulation. This system consists of two main software modules: a feature curve extraction module and a multi-body dynamic simulation module. The feature curve extraction module extracts the feature curve list of the entire transportation path with swept path analysis to transmit the information to the simulation module. The multi-body dynamic simulation module extracts the constraint condition for the product design process that a designer uses as the transportation constraint in the design process or quick checks the transportability of the product files. The entire structure of the system is accessible by a web-based platform. When the user (designer) inputs the product files and the transportation information, the system gives the constraints and transportability to the user. The entire analysis is performed by a background process on the analysis server. We also propose a multimodal transportability evaluation algorithm that considers design and dynamic conditions.

In order to manage and control the quality of products in a real-time manner, a statistical process control methodology, one of the most popular quality control activities, is utilized as a software service in many manufacturing industries. However, these quality control systems are not affordable to small and medium sized industries due to their expense in installation, difficulty, inflexibility and lack of post management in the systems. To satisfy all the various requested given by the enterprises, we propose a customizable web-based process analysis system with user-centered design that enables continuous process management. The methodologies for constructing such a customizable web-based process analysis system are suggested first. Then, these methodologies are implanted into the suggested system, Process analysis system (PAS) and each process is described in detail. PAS is utilized in the concrete manufacturing process to observe a continuous process improvement. Lastly, the suggestions for PAS in future are discussed in this paper.

The electrohydrodynamic (EHD) inkjet method is a printing technology using electricity. This technique allows for the printing of EML (Emission layer) materials, usually used for OLED devices, on a substrate. In this study, ejection experiments were performed with various solvents to verify which of them is properly ejected in the EHD method. The solvents employed were dielectric liquids with low viscosity and it was confirmed that among them two solvents, 1,2-Dichlorobenzene (DCB) and 1,2-Dichloroethane (DCE), produced the pulsating cone-Jet mode and stable cone-jet mode well. In addition, experiments were conducted to find out how the voltage and applied flux influence the ejection mode, in order to apply the result to the ejection control. It was found that the selected solvent was easily ejected and printed, due to the free surface charge and charge density determined by the dielectric constant. Finally, a patterning experiment was performed to verify proper printing.

A user’s haptic experience in using an electronic device is related to the continuous and dynamic variances of the structural state of the device. Since the changes of the structural component cause complex changes of the dynamics, it is difficult to predict the user’s experience. We propose a function-behavior-structure framework to predict and improve the user’s experience. The framework consists of the function layer model, the behavior layer model, and the structure layer model. Especially, the independent behavior model to the device is based on a physical phenomenon. Finally, an optimized structure which produces an ideal haptic experience for a cell phone is suggested.

This research was conducted to understand friction and wear due to mechanical and electrical contact between a brush and a commutator, which play an important role in driving brush-type DC motors, and to predict the motor life through brush wear. To identify the influencing factors, the variables used in the experiments were the operating voltage, the current load, the rotational speed, and the environmental temperature. The design of experiment used for the analysis of brush wear was based on the Taguchi method. An independent condition was provided for each factor during the experiment. The results indicate the high contribution of current and temperature to the wear behavior. The operation voltage showed negligible influence on the wear behavior.

Some critical components in motors and generators have sliding electrical contacts. Electrical brushes are commonly used in those contact points to conduct current between the stationary and moving parts of a motor. Brushes are exposed both to mechanical and electrical loading. In this paper, studies on the wear of brushes against copper commutator were briefly reviewed. The main influential factors of brush wear are associated with both mechanical and electrical wear. Brush wear is affected by various factors including temperature, material properties, sliding speed, contact force, and interfacial and environmental conditions. The mechanical wear of brushes is proportional to the brush spring pressure and sliding speed, while the electrical wear of brushes is associated with current and contact voltage drop. To characterize the wear, a brush wear test machine was designed, and influential factors were measured such as electrical contact resistance, temperature, wear mass loss, and so on. The wear tests were processed using a small brush type automotive DC motor. The main objective of this study is to investigate the effects of the wear behavior of copper-graphite brushes in a small brush-type DC motor. The variable conditions are with and without electrical current by changing the brush spring pressure and sliding speed, and the results are electrical contact resistance, voltage drop, brush surface temperature rise, and so on. Brush wear greatly changes with electrical current. This shows that the high current not only produces more Joule heating but also causes an increase in voltage drop, which will result in additional Joule heating.