• International journal of advanced smart convergence
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• v.12 no.2
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• pp.47-55
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• 2023

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R² score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.1
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• pp.94-100
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• 2024

Recently, as interest in personal hygiene has increased due to the community spread of COVID-19 and variant viruses, fixed and potable UV germicidal equipment to sterilize indoor spaces and hand-held UV germicidal equipment to sterilize household items such as masks and mobile phones are continuously being developed and sold. However, the development and sales of the product are difficult because appropriate testing methods have not yet been established. In this situation, if an uncertified product is distributed in the market, it can cause serious harm to consumers. In this study, we investigate the photobiological risks and safety devices against UV exposure of UV germicidal equipment distributed domestically, and propose appropriate test methods for portable UV germicidal equipment based on the research results.

• Journal of Ocean Engineering and Technology
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• v.37 no.5
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• pp.198-204
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• 2023

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

• Journal of Bio-Environment Control
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• v.23 no.1
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• pp.26-30
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• 2014

Purpose of this study was a development of a sprayer arm auto control system that could be operated according to distance from pear trees for automation of pest control. Auto control system included two parts, hardware and software. First, controller was made with an MCU and relay switches. Two types of ultra-sonic sensors were installed to measure distance from pear trees: one on/off type that detect up to 3 m, and the other continuous type providing 0~5 V output corresponding to distance of 0~3 m. Second, an auto control algorithm was developed to control. Each spraying arm was controlled according to the sensor-based distance from the pear trees. And it could dodge obstacles to protect itself. Max and min signal values were eliminated, when five sensor signals was collected, and then signals were averaged to reduce sensor's noises. According to results of field experiment, auto control test result was better than non auto control test result. Spraying rates were 69.25% (left line) and 98.09% (right line) under non auto control mode, because pear trees were not planted uniformly. But, auto control test's results were 92.66% (left line) and 94.64% (right line). Spraying rate was increased by maintaining distance from tree.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.83-90
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• 2006

The purpose of this paper is to describe the design and implementation of an unmanned ground vehicle, called the TailGator at CIMAR (Center for Intelligent Machines and Robotics) of the University of Florida. The TailGator is a gas powered, four-wheeled vehicle that was designed for the AUVSI Intelligent Ground Vehicle Competition and has been tested in the contest for 2 years. The vehicle control model and design of the sensory systems are described. The competition is comprised of two events called the Autonomous Challenge and the Navigation Challenge: For the autonomous challenge, line following, obstacle avoidance, and detection are required. Line following is accomplished with a camera system. Obstacle avoidance and detection are accomplished with a laser scanner. For the navigation challenge, waypoint following and obstacle detection are required. The waypoint navigation is implemented with a global positioning system. The TailGator has provided an educational test bed for not only the contest requirements but also other studies in developing artificial intelligence algorithms such as adaptive control, creative control, automatic calibration, and internet-base control. The significance of this effort is in helping engineering and technology students understand the transition from theory to practice.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.61-66
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• 2020

Since the ocean accounts for 70.8 percent of the earth's surface, the success of IoT technology in the marine industry is to collect information from devices placed in a wider range. LPWA is a feature with a wide range of communication and is very suitable for deployment in the ocean. In this paper, the real-sea performance distance experiment was carried out based on Private LoRa, a key technology for executing the electronic phrase real-name system. A private LoRa module based on sx1276 was developed, and Gateway was developed to transfer data received by private LoRa to the server using SKT Cat. M1. After installing gateways at 599 meters above sea level and experimenting with data transmission and reception at 25 km, 40 km and 60 km, we were able to see that the communication success rate was obtained to be 96.1%. 97.1% and 96.2% respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2601-2606
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• 2012

In this paper, an electricity management system, which saves energy by utilizing electricity consumption of load from an environment that uses at least two compact generators, is proposed and developed. A hardware is constructed by using TMS320C6713 DSP chip made by TI that is capable of high speed hardware floating point processing while serial communication is used for communication with a monitoring PC. Manual control is made possible from the monitoring PC and automatic on/off is enabled in the generator by using data collected by CT/PT sensor from the DSP mainboard. Test results confirm that the electricity management system proposed in this study functions without abnormality. The application of an algorithm that saves energy by using electricity consumption of load also allows for a longer supply of electricity compared to continuously using two compact generators.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.681-685
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• 2000

A computer software was developed in our laboratory to automatically control the pigs environment by the image analysis system (IAS), which monitors and analyzes the pig's behavior and feeds the results back to the computer hardware. Three feeding trials were conducted with growing pigs ($L{\times}Y$) to test the effectiveness of the IAS under various seasons. In all three trials, the open-sided conventional pens with half-slatted floor were used as controls; for the IAS treatment, fully-slatted floors were used in the windowless pens. Experiment 1 was conducted in the winter for 30 d with 24 growing pigs. There were two treatments (Conventional vs. IAS), and three pens (replicates) per treatment. During the growing period, the feed efficiency was significantly (p<0.05) improved by the IAS. In addition, the pigs reared under the IAS during the growing period displayed better growth rate during the finishing period than did the pigs reared under the conventional conditions. Experiment 2 was conducted in the summer for 30 d with 24 growing pigs. The experimental design was the same as Experiment 1. During the finishing period, all the pigs were kept in conventional open-sided pens until their market weights to evaluate their carcass characteristics. During the growing period, the growth rate and feed efficiency of the pigs in the IAS was better than those of the control pigs. In addition, various carcass characteristics were significantly improved by the IAS rearing during the growing period. Experiment 3 was conducted with 30 growing pigs for 30 d in the spring. The experimental design was the same as Experiment 1. No difference was found in growing performance between the control and IAS pigs. It could be concluded that the IAS is effective in providing optimum conditions for the growing pigs in summer and winter seasons. In addition, providing an optimum environment during the growing period results in improved growth rate, feed efficiency, and carcass qualities for the finishing pigs.