• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.54-61
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• 2008

This paper presents a method for reducing modelling error in the linear motion system with voicecoil actuator (VCA). A model of linear motion system composed of a mechanism and control was prepared to verify the proposed method. In modeling of the system, the damping coefficient obtained experimentally is applied to the model in order to consider the effect of the viscous friction for the moving part in VCA. The response velocity of VCA for duty ratio of PWM signal was analyzed in the time domain. Consequently, the relation between velocity and duty ratio was obtained. The result from the experiment showed an error of 9% when compared with that of simulation. In order to reduce the modeling error, impedance variation according to input frequency was analyzed, and equivalent impedance with multi-frequency was applied to the control part. As a result, the modeling error decreased to 5%.

• Journal of IKEEE
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• v.24 no.4
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• pp.921-930
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• 2020

The Counter-battery radar estimates the origin and impact point of the artillery by tracking the trajectory of the shell. In addition, it has the ability of identifying the type of weapon. Depending on the position between the shell and the radar, the detected signals appear differently. This has ambiguity to distinguish the type of shells. This paper compares fuzzy logic and artificial intelligence, which classifies type of shell using the parameter of signal processing step. According to the research result, artificial intelligence can improve identification rate of type of shell. The data used in the experiment was obtained from a live fire detection test.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.7-14
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• 2024

This study conducted a comparative experiment on the continuity of time series data and the classification performance of artificial intelligence models. In BCI research using EEG signals, the performance of behavior and thought classification improved as the continuity of the data decreased. In particular, LSTM achieved a high performance of 0.8728 on data with low continuity, and DNN showed a performance of 0.9178 when continuity was not considered. This suggests that data without continuity may perform better. Additionally, data without continuity showed better performance in task classification. These results suggest that BCI research based on EEG signals can perform better by showing various data characteristics through shuffling rather than considering data continuity.

• Korean Journal of Artificial Intelligence
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• v.4 no.1
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• pp.4-7
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• 2016

These days, citizens have made change of food life to take Western style food and to suffer from diabetes because of excessive nutrition taking, less exercise, stress and other environmental factors. They may suffer from diabetes because of genetic defect, surgery of pancreas, disinfection and medicine and others. One of ten Koreans may have symptom of diabetes to be popular. The diabetes that is a kind of metabolic disease has high blood sugar at disorder of hyper insulinism and/or defect of insulin action. Long time high blood sugar may produce chronic disease of kidney, eyes, nerve, heart and blood vessel and others. The purpose of health care of diabetes patient was to reach target blood sugar by diet, physical exercise and medicine and to prevent and delay complication. Diabetes patient shall control blood sugar to keep healthy. The blood sugar control requires time and effort, and all of the patients are difficult to make effort and to spend time. You can control blood sugar by the application. The application allows patients to control blood sugar and to save time and efforts and to make small sized input and automation of remaining area. The service was limited to blood sugar graph, and user carries smart phone to conduct test and to have difficulty. Further study needs to solve the problems and to investigate blood sugar testing not carrying smart phone and to make application of easy control of blood sugar.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.176-183
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• 2009

This paper proposes command signal generating method for a wearable robot using the force as the input signal. The basic concept of this system pursues the combination of the natural and sophisticated intelligence of human with the powerful motion capability of the robot. We define a task for the command signal generation to operate with the human body simultaneously, paying attention to comfort and ease of wear. In this study, we suggest a basic exoskeleton experimental system to evaluate a HRI(Human Robot Interface), selecting interfaces of arm braces on both wrists and a weight harness on the torso to connect the robot and human. We develop the HRI to provide a command for the robot motion. It connects between the human and the robot with the multi-axis load-cell, and it measures the relative force between the human and the robot. The control system calculates the trajectory of end-effector using this force signal. In this paper, we verify the performance of proposed system through the motion of elbow E/F(Extension/Flexion), the shoulder E/F and the shoulder Ab/Ad (Abduction/Adduction).

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.83-93
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• 2006

This paper is wireless location awareness technology using RFID. We investigates the Location of the received Signal strength reported by RF Analyses of the data are performed to understand the underlying features of location fingerprints. The system is performed factors the extreme environmental Emit signal, which consists of a unique 5000 Terminals. The Location Service have become very popular in many service industries, purchasing and distribution logistics, industry, manufacturing companies and a parking place. The Technically optimal Solution would be the storage of Intelligence information in the most common form of electronic data-carrying device in use in everyday life is the smart card based upon a contact field (telephone smart card, bank cards). The method of an indoor positioning experiment system is compared using measured Location data and a charge of service. The result of research showed the following: first, to check out the mechanism between benefit of system installation and operation of Active RFID. Second, it contributed on indoor wireless location intelligence system efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.173-178
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• 2022

Recently, various computing technologies such as artificial intelligence, big data, and IoT are developing. In particular, artificial intelligence-based deepfake technology is being used in various fields such as the content and medical industry. Deepfake technology is a combination of deep learning and fake, and is a technology that synthesizes a person's face or body through deep learning, which is a core technology of AI, to imitate accents and voices. This paper uses deepfake technology to study the creation of virtual characters through the synthesis of animation models and real person photos. Through this, it is possible to minimize various cost losses occurring in the animation production process and support writers' work. In addition, as deepfake open source spreads on the Internet, many problems emerge, and crimes that abuse deepfake technology are prevalent. Through this study, we propose a new perspective on this technology by applying the deepfake technology to children's material rather than adult material.