• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.55-62
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• 2015

In this paper we propose a method to analyze user's motion as a game command, and implement a sports serious game applied the motion analysis method as a command interpreter. Recently, various contents platforms appear in industrial market, the computer game contents plays an important role in these emerging platforms as a killer contents. The computer game has enough values as an independent major cultural product, moreover it has the potential to be applied in various other fields such as education, healthcare, training, and so on. It could motivate users to do something continuously, and it could also support an immersive environment in a certain special game contents such as VR game. The Serious game 'ChoDeungGangHo', implemented in this paper, is the sensory healthcare serious game based on 3D run game and fitness game. It is designed for user to train the various exercise element by just playing the game, and it also supports the user management system and the linkage of social media. We proposes the sensory serious game 'ChoDeungGangHo' as a model of commercial serious game.

• Journal of Practical Engineering Education
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• v.6 no.2
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• pp.91-97
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• 2014

Education equipment for field programmable gate array (FPGA) based multimedia player design is introduced. Using the education equipment, an example of hardware design for color detection and augment reality (AR) game is described, and an example of syllabus for "Digital system design using FPGA" course is introduced. Using the education equipment, students can develop the ability to design some hardware, and to train the ability for the creative capstone design through conceptual, partial-level, and detail designs. By controlling audio codec, system-on-chip (SOC) design skills combining a NIOS II soft microprocessor and digital hardware in one FPGA chip are improved. The ability to apply wireless communication and LabView to FPGA-based digital design is also increased.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.157-164
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• 2000

This paper presents a method to detect a rotor position and to drive a BLDC motor from standstill to medium speed without any position sensor comparing the current responses due to the inductance variation in the rotor position. A rotor position at a standstill is identified by the current responses of six pulses injected to each phase of a motor. Once the motor stars up pulse train that is composed of long and short pulses is injected to the phase corresponding to produce the maximum torque and the next phase continuously. it provides not only the torque but also the information of the next commutation time effectively when the response of long and short pulses crosses each other after the same time delay. This method which is verified experimentally using a DSP can drive a BLDC motor to the medium speed smoothly without any rattling and time delay compared with the conventional sensorless algorithm.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.245-249
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• 2006

The aim of this study is to design and develop the gait rehabilitation equipment that judge patient's movement of his/her center of gravity using pressure sensors, and to aid hemiplegic patients to balance themselves using an automatic stepper that changes the patient's center of gravity. It is hard to bear the weight on the affected side for hemiplegic patients. The gait rehabilitation equipment detects the footing phase of hemiplegic patient during training and moves the unaffected footing side of the stepper up and moves the affected footing side down simultaneously so that the patient's center of gravity can shift from unaffected side to affected side. The gait rehabilitation system was developed and applied for hemiplegic patients during exercise. Eight hemiplegic patients and one normal adult were studied. The developed gait rehabilitation system could judge not only the normal adult's intention but also the patient's intention to move his/her center of gravity. Even though the most of hemiplegic patients exercised in automatic mode and a few hemiplegic patients exercised in manual mode, the developed gait rehabilitation system can aid the hemiplegic patients to train more easily.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.56-63
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• 2021

As the use of robots in service area increases, research has been conducted to replace human tasks in daily life with robots. Among them, this study focuses on the tidy-up task on a desk using a robot arm. The order in which tidy-up motions are carried out has a great impact on the success rate of the task. Therefore, in this study, a neural network-based method for determining the priority of the tidy-up motions from the input image is proposed. Reinforcement learning, which shows good performance in the sequential decision-making process, is used to train such a task planner. The training process is conducted in a virtual tidy-up environment that is configured the same as the actual tidy-up environment. To transfer the learning results in the virtual environment to the actual environment, the input image is preprocessed into a segmented image. In addition, the use of a neural network that excludes unnecessary tidy-up motions from the priority during the tidy-up operation increases the success rate of the task planner. Experiments were conducted in the real world to verify the proposed task planning method.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.587-594
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• 2021

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.868-876
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• 2021

Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.