• Journal of Drive and Control
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• v.19 no.2
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• pp.1-10
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• 2022

Field robots operate in various areas, including construction, agriculture, forestry and manufacturing. Typical tasks of field robots used in various areas include excavation, flattening, and demolition. Such tasks are often accomplished in narrow alleys or indoors. In the case of field robots, there is a limit to working in a small space. Thus, to compensate for these shortcomings, many field robots equipped with Tiltrotators have recently been observed. The advantages of Tiltrotator are improved task efficiency and reduced operating time by reducing unnecessary behavior. We need simulation models that can improve the ability of new people to work and simulate tasks in advance. Thus, in this paper, we developed a simscape-based simulation model and modeling of 6DOF systems for field robots equipped with Tiltrotator. Dynamic modeling of field robot 3D models using Simcape multibody and hydraulic systems of field robots using Simcape Hydraulics were modeled. We applied a PID controller to create a control system that operates along the input angle. Simulation results show that errors occur when comparing input and output angles, but overall, they move along input angles.

• Journal of the Ergonomics Society of Korea
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• v.34 no.4
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• pp.335-351
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• 2015

Objective: The aim of this study was to perform a usability test for CPAD (Cognitive Perceptual Assessment for Driving) and improve it based on the test results. Background: The cognitive perceptual assessment for driving is a computer-based assessment tool to assess the driving capacity of people with brain-damages. It may be a good tool for evaluating the brain-damaged drivers' safe driving capabilities and screening cognitive and perceptual deficits related to driving. We performed a usability test to improve the CPAD based on the result. Method: Both the software consisting of 8 sub-tests (depth perception, sustained attention, divided attention, stroop test, field dependency, digit span, trail making A, trail making B) and the hardware including the input and output devices ( joystick, mouse, keyboard, touch screen) were evaluated through user interviews. Also we identified the problems and issues in using them by observing the participants performing the CPAD tasks. Results: Based on the results, the task instructions were visually and auditorily improved and more practice examples were added to help the users understand the instructions better and increase the input accuracy, a response time window was added and the joystick and touch screen were simplified, which made it easier for the user to perform the CAPD tasks. Conclusion: User discomforts were minimized by improving the task environment, unless it had affected the evaluation criteria. Application: We plan to distribute the improved version of the CPAD to the regional rehabilitation hospitals, and the driving support centers for people with disabilities throughout the country, so it could be used as an evaluation tool for disabled drivers' cognitive and perceptual functions.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.308-317
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• 2022

Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.2
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• pp.42-53
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• 2014

Robust voice recognition (RVR) is essential for a robot to communicate with people. One of the main problems with RVR for robots is that robots inevitably real environment noises. The noise is captured with strong power by the microphones, because the noise sources are closed to the microphones. The signal-to-noise ratio of input voice becomes quite low. However, it is possible to estimate the noise by using information on the robot's own motions and postures, because a type of motion/gesture produces almost the same pattern of noise every time it is performed. In this paper, we propose an RVR system which can robustly recognize voice by adults and children in noisy environments. We evaluate the RVR system in a communication robot placed in a real noisy environment. Voice is captured using a wireless microphone. Navigation Strategy is shown Obstacle detection and local map, Design of Goal-seeking Behavior and Avoidance Behavior, Fuzzy Decision Maker and Lower level controller. The final hypothesis is selected based on posterior probability. We then select the task in the motion task library. In the motion control, we also integrate the obstacle avoidance control using ultrasonic sensors. Those are powerful for detecting obstacle with simple algorithm.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.41-44
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• 2007

In this paper, we introduce a system in which car navigation scenario is plugged multimodal interface based on multimodal middleware. In map-based system, the combination of speech and pen input/output modalities can offer users better expressive power. To be able to achieve multimodal task in car environments, we have chosen SCXML(State Chart XML), a multimodal authoring language of W3C standard, to control modality components as XHTML, VoiceXML and GPS. In Network Manager, GPS signals from navigation software are converted to EMMA meta language, sent to MultiModal Interaction Runtime Framework(MMI). Not only does MMI handles GPS signals and a user's multimodal I/Os but also it combines them with information of device, user preference and reasoned RDF to give the user intelligent or personalized services. The self-simulation test has shown that middleware accomplish a navigational multimodal task over multiple users in car environments.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.986-993
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• 2017

Object Tracking is a technique for tracking moving objects over time in a video image. Using object tracking technique, many research are conducted such a detecting dangerous situation and recognizing the movement of nearby objects in a smart car. However, it still remains a challenging task such as occlusion, deformation, background clutter, illumination variation, etc. In this paper, we propose a novel deep visual object tracking method that can be operated in robust to many challenging task. For the robust visual object tracking, we proposed a Convolutional Neural Network(CNN) which shares weight of the convolutional layers. Input of the CNN is a three; first frame object image, object image in a previous frame, and current search frame containing the object movement. Also we propose a method to consider the motion of the object when determining the current search area to search for the location of the object. Extensive experimental results on a authorized resource database showed that the proposed method outperformed than the conventional methods.

• Journal of the Ergonomics Society of Korea
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• v.27 no.1
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• pp.29-35
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• 2008

An estimated 1 billion mobile phone were sold globally in the year 2006. In Korea, people watch television 3.17 hours in a day. Television isn't what it used to be. Digital TV provides both interactivity and high definition. Mobile phone also transferred from 2G to 3G or 3.5G. This means the complexity of TV and mobile phone is increased, design of user interface is more difficult. Unlike the personal computer industry, TV and mobile phone industries have no standard user interface. A comparative study for user interface between TV and mobile phone is studied. User, task, system are analyzed in requirement analysis. User interface model and interaction are also analyzed between TV and mobile phone. This study provides some insights for user interface design. First, the UI designer have to consider another products because one user using one product at the same time using another products. Experience for one product affects that for another product. Second, TV and mobile phone show very similar pattern, especially interaction task and input interaction. Third, there are not sometimes optimized experience between service operator and device manufacturer. Cooperative design between them is required.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.49-54
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• 2009

Visual occlusion method is a visual demand measuring technique which uses periodic vision/occlusion cycle to simulate driving environment. It became one of the most popular techniques for the evaluation of in-vehicle interfaces due to its robustness and cost-effectiveness. However, it has a limitation in that the vision/occlusion cycle forces the user to use the IVIS at a predetermined pace, while a driver decides when to use the device on his/her own in actual driving. This paper proposes a user-driven visual occlusion method for measuring the visual demand of in-vehicle interfaces. An experiment was conducted to examine the visual demand of an in-vehicle interface prototype using both the existing (system-driven) occlusion method and the proposed (user-driven) one. Two in-vehicle tasks were evaluated: address input and radio tuning. The results showed that, for the radio tuning task, there were significant differences in total shutter open time and resumability ratio between the methods. The user-driven visual occlusion method not only allows a better representation of drivers' behavior, but it also seems to provide more information on the chunkability of a task.