• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.451-459
• /
• 2018

This work presents an approach for modeling of electric vehicle considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The system is composed of IPMSM (Interior Permanent Magnet Synchronous Motor) coupled with the wheels through a drive train. Generally, IPMSM is controlled by ordinary PID controllers. Performance of the ordinary PID controller is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new type of fuzzy logic gain tuner for PID controllers of IPMSM is required. Therefore, in this paper fuzzy logic based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicle with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were carried out and results were observed. The simulation results have proved that the proposed control system works well to remove the transient oscillations and assure better system response in all conditions.

• Proceedings of the KIEE Conference
• /
• 1994.11a
• /
• pp.172-174
• /
• 1994

Due to the environmental considerations and the energy crisis, there has been a revival of electric vehicles since 1960s. Research and development work concerning with electric vehicles (EVs) was becoming more intense in last decade. As compared with conventional internal combustion engine (ICE) cars, EVs have the advantages of clean, quiet, better energy efficiency, less maintenance and improving the load factor of electric power systems. However, EVs usually have a snort running range, bad acceleration performance and high initial cost. The main reason for these shortcomings is the low figure of energy density and the high per energy cost of battery at present technology state. So it is very important to optimize the overall drive system design with respect to the maximum utilization of battery, energy, motor torque and inverter power. This paper describes a demonstration model of electric car which is driven by 4-wheel direct method using the vector control.

• Advances in concrete construction
• /
• v.8 no.2
• /
• pp.77-90
• /
• 2019

This paper aims to investigate vibration frequency decrease (vibration period elongation) of reinforced concrete (RC) structure with unreinforced infill wall and reinforced infill wall exposed to progressively increased artificial earthquake load on shaking table. For this purpose, two shaking table experiments were selected as a case study. Shaking table experiments were carried on 1:1 scaled prototype one bay one storey RC structure with infill walls. The purpose of this shaking table experiment sequence is to assess local behavior and progressive collapse mechanism. Frequency decrease and eigen-vector evolution are directly related to in-plane and out-of-plane bearing capacities of infill wall enclosure with reinforced concrete frame. Firstly, frequency decrease-damage relationship was evaluated on the base of experiment results. Then, frequency decrease and stiffness degradation were evaluated with applied Peak Ground Acceleration (PGA) by considering strength deterioration. Lastly, eigenvector evolution-local damage and eigenvector evolution-frequency decrease relationship was investigated. Five modes were considered while evaluating damage and frequency decrease of the tested specimens. The relationship between frequency decrease, stiffness degradation and damage level were presented while comparing with Unreinforced Brick Infill (URB) and Reinforced Infill wall with Bed Joint Reinforcement (BJR) on the base of natural vibration frequency.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.617-624
• /
• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.249-251
• /
• 2021

In this paper, we introduce the deep-learning system using Tensorflow for recognizing situations that can occur fall situations when the elderly are moving or standing. Fall detection uses the LSTM (long short-term memory) learned using Tensorflow to determine whether it is a fall or not by data measured from wearable accelerator sensor. Learning is carried out for each of the 7 behavioral patterns consisting of 4 types of activity of daily living (ADL) and 3 types of fall. The learning was conducted using the 3-axis acceleration sensor data. As a result of the test, it was found to be compliant except for the GDSVM(Gravity Differential SVM), and it is expected that better results can be expected if the data is mixed and learned.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.391-393
• /
• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.

• Design & Manufacturing
• /
• v.17 no.3
• /
• pp.34-40
• /
• 2023

In this study, real-time collection of mold vibration signals during injection molding processes was achieved through IoT devices installed on the mold surface. To analyze changes in the collected vibration signals, injection molding was performed under six different process conditions. Analysis of the mold vibration signals according to process conditions revealed distinct trends and patterns. Based on this result, cosine similarity was applied to compare pattern changes in the mold vibration signals. The similarity in time and acceleration vector space between the collected data was analyzed. The results showed that under identical conditions for all six process settings, the cosine similarity remained around 0.92±0.07. However, when different process conditions were applied, the cosine similarity decreased to the range of 0.47±0.07. Based on these results, a cosine similarity threshold of 0.60~0.70 was established. When applied to the analysis of mold vibration signals, it was possible to determine whether the molding process was stable or whether variations had occurred due to changes in process conditions. This establishes the potential use of cosine similarity based on mold vibration signals in future applications for real-time monitoring of molding process changes and anomaly detection.

• Journal of Korea Multimedia Society
• /
• v.12 no.3
• /
• pp.335-344
• /
• 2009

Embedded systems and web browsers have started to provide two-dimensional vector graphics features, to finally support scalability of graphics outputs, while traditional graphics systems have focused on the raster and bitmap operations. Nowadays, SVG and Flash are actively used while OpenVG from Khronos group plays the role of a de facto low-level API standard to support them. In this paper, we represent the design and implementation process and the final results of an OpenVG implementation, AlexVG. From its design stage, our implementation aims at the cooperation with SVG-Tiny, another de facto standard for embedded systems. Currently, our overall system provides not only the OpenVG core features but also variety of OpenVG application programs and SVG-Tiny media file playing capabilities. For the conformance with the standard specifications, our system completely passed the whole OpenVG conformance test suites and the graphics output portions of the SVG-Tiny conformance test suites. From the performance point of view, we focused on the efficiency and effectiveness especially on the mobile phones and embedded devices with limited resources. As the result, it showed impressive benchmarks on the small-scale CPU's such as ARM's, even without neither any other libraries nor acceleration hardware.

• The Journal of Society for e-Business Studies
• /
• v.21 no.1
• /
• pp.147-163
• /
• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.11
• /
• pp.965-972
• /
• 2016

This paper presents results of flight experiments on a navigation algorithm including multiplicative extended Kalman filter for estimating attitude of the guided munition. The filter describes orientation of aircraft by data fusion with low-cost sensors where measurement update is done by multiplication, rather than addition, which is suitable for quaternion representation. In determining attitude from vector observations, the existing approach utilizes a 3-axis accelerometer as a 2-axis inclinometer by measuring gravity to estimate pitch and roll angles, while GNSS velocity is used to derive heading of the vehicle. However, during accelerated maneuvers such as coordinated flight, the accelerometer provides inadequate inclinometer measurements. In this paper, the measurement update process is newly defined to complement the vulnerability by using different vector observations. The acceleration measurement is considered as a result of a centrifugal force and gravity during turning maneuvers and used to estimate roll angle. The effectiveness of the proposed method is verified through flight experiments.