• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.05a
• /
• pp.978-980
• /
• 2013

In this paper, we have presented a basic idea of a power shut-off client based on NFC (near field communication). For the first step of the system design, we have shown the conceptual diagrams of the hardware configuration and the software logic flow. This system can be applied to the integrated power control in home, office, school, factory, and apartment. The standby power shut-off system will bring saving in electrical energy and cost.

• IE interfaces
• /
• v.23 no.4
• /
• pp.349-356
• /
• 2010

The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.7
• /
• pp.2913-2918
• /
• 2011

This paper proposed the friction model using Preisach algorithm with neural network based on experimental results. In order to apply the neural network algorithm, the back propagation update rule was used and the updated weighting factor of neural network was applied to distribute function of Preisach model. In order to implement the proposed algorithm, the LabView software was used to apply to the precision control of mechanical system. The evaluation of the proposed friction model was executed through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.661-664
• /
• 2009

Recently, intelligent surveillance camera systems are needed popularly. However, current researches are focussed on improvement of a single module rather than implementation of an integrated system. In this paper, we implemented a moving wireless surveillance camera system which is composed of face detection, and using motion sensor. In our implementation, we used a camera module from SHARP, a pair of wireless video transmission module from ECOM, body of moving robot used for A4WD1 Combo kit for RC, a pair of ZigBee RF wireless transmission module from ROBOBLOCK, and a motion sensor module (AMN14111) from PANASONIC. We used OpenCV library for face dection and MFC for implement software. We identified real-time operations of face detection, PTT control, and motion sensor detecton. Thus, the implemented system will be useful for the applications of remote control, human detection, and using motion sensor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.975-976
• /
• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.4
• /
• pp.129-134
• /
• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06a
• /
• pp.227-229
• /
• 2012

한국해양연구원에서 개발하고 있는 해저보행로봇 CR200은 6개의 다리를 이용하여 해저면에서 보행으로 이동하며 정밀 해저탐사 및 작업을 수행할 수 있는 로봇이다. 운용자는 선상제어실에서 유선으로 연결된 CR200을 원격으로 모니터링하거나 제어한다. 특히 안정적인 보행을 위해서는 100Hz의 주기로 CR200의 상태 정보가 선상제어실로 전송되고, 선상제어실에서는 전송된 정보를 기반으로 제어 명령을 산출하여 CR200으로 전송해야 한다. 이러한 주기적인 실시간 제어를 위해서는 일반적으로 실시간 운영체제를 사용하지만, 본 논문에서는 실시간 운영체제를 사용하지 않고 시스템 시간 기반의 백그라운드 프로세스로 동작하는 소프트웨어 타이머를 사용하여 실시간 제어를 하는 방법을 제안하고, 성능 평가 결과를 제시한다. 제안한 방법의 실시간 속성을 검증하기 위하여, 현재 설계에서 운영체제로 고려하고 있는 우분투 10.04와 윈도우즈 7을 CR200에 탑재되는 Advantech 사의 PCM-3362 보드에 설치하여 소프트웨어 타이머의 성능을 10ms부터 100ms까지 각각 실험하였다. 실험결과에 따르면, 두 운영체제에서 모두 누락이 없이 타이머 동작이 수행되었으며, 10ms 간격으로 타이머를 동작하였을 때에 우분투에서는 평균 오차가 $41{\mu}s$이었고 윈도우즈 7에서는 7.7ms였다. 윈도우즈 7에서의 오차는 100Hz 제어 주기에 사용하기에 적합하지 않지만, 우분투에서의 오차는 제어 주기 간격의 0.41%에 불과하기 때문에 해저보행로봇의 실시간 제어에 영향을 주지 않는 오차이다. 따라서 CR200의 임베디드 컴퓨터와 선상제어실의 원격제어 컴퓨터는 우분투 운영체제 상에서 소프트웨어 타이머를 이용하여 상호 연동되도록 구현할 예정이다.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.12
• /
• pp.129-137
• /
• 2013

As the usage of computer based systems continues to increase in our normal life, there are constant efforts to enhance the accessibility of information for handicapped people. For this, it is essential to develop new interface ways for physical disabled peoples by means of human-computer interface (HCI) or human-machine interface (HMI). In this paper, we developed HMI using electromyogram (EMG) and electrooculogram (EOG) for people with physical disabilities. Developed system is composed of two modules, hardware module for signal sensing and software module for feature extraction and pattern classification. To maximize ease of use, only two skin contact electrodes are attached on both ends of brow, and EOG and EMG are measured simultaneously through these two electrodes. From measured signal, nine kinds of command patterns are extracted and defined using signal processing and pattern classification method. Through Java based real-time monitoring program, developed system showed 92.52% of command recognition rate. In addition, to show the capability of the developed system on real applications, five different types of commands are used to control ER1 robot. The results show that developed system can be applied to disabled person with quadriplegia as a novel interface way.

• Journal of KIISE:Software and Applications
• /
• v.36 no.10
• /
• pp.836-850
• /
• 2009

Human posture recognition has attracted tremendous attention in ubiquitous environment, performing arts and robot control so that, recently, many researchers in pattern recognition and computer vision are working to make efficient posture recognition system. However the most of existing studies is very sensitive to human variations such as the rotation or the translation of body. This is why the feature, which is extracted from the feature extraction part as the first step of general posture recognition system, is influenced by these variations. To alleviate these human variations and improve the posture recognition result, this paper presents 3D Star Skeleton and Principle Component Analysis (PCA) based feature extraction methods in the multi-view environment. The proposed system use the 8 projection maps, a kind of depth map, as an input data. And the projection maps are extracted from the visual hull generation process. Though these data, the system constructs 3D Star Skeleton and extracts the rotation invariant feature using PCA. In experimental result, we extract the feature from the 3D Star Skeleton and recognize the human posture using the feature. Finally we prove that the proposed method is robust to human variations.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.2
• /
• pp.107-116
• /
• 2016

Industrial robots have been usually controlled using text-based programming languages provided by each manufacturer with its button-based TP(Teaching Pendent) terminal. Unfortunately, when we consider that people who manipulate TPs in manufacturing sites are mostly unskilled with no background knowledge about computer programming, these text-based programming languages using button-based interaction on manufacturing sites are too difficult for them to learn and use. In order to overcome the weaknesses of the text-based programming language, we propose a visual programming language that can be easily used on gesture-enabled devices. Especially, in our visual programming environment, each command is represented as a block and robots are controlled by stacking those blocks using drag-and-drop gestures, which is easily learnable even by beginners. In this paper, we utilize a widely-spread device, Tablet PC as the gesture-enabled TP. Considering that Tablet PC has limited display space in contrast to PC environments, we designed different kinds of sets of command blocks and conducted user tests. Based on the experiment results, we propose an effective set of command blocks for Tablet PC environment.