• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• Journal of National Security and Military Science
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• s.5
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• pp.1-45
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• 2007

Reserved forces of ROKA are in charge of replacement of TOE in the wartime and mission of rear area operation. But there is institutional inertia in the law and organization oriented to fill human resources rather than take mission. We need to prepare for the investment and arrangement of reserved forces as military power that would be replaced standing forces. In this portion, to reinforce reserve forces elite, First, efficient mobilization regulations and systems are suggested. I covered a maintenance of relevant mobilization ordinances which need to legislated and approved by national assembly for wartime and development of mobilization system which might lose the appropriate time for mobilization due to complicated declaration procedures and measures to overcome the panic at the initial stage of the war and organization and employment of nationwide transportation system and mobilization center. To ensure efficient resource management and mobilization of reserve forces with a number of approximately 3 million, there's a necessity of organization for integration and conciliation. To make it real, I suggested establishing and employing the mobilization center, on first phase, employ the mobilization center focusing on homeland divisions, on second phase, it is advisable to convert to national level mobilization system and develop to central mobilization center focusing on national emergency planning committee. During peacetime, in conjunction with Mobilization Cell, mobilization center can conduct resource survey and integrate and manage mobilization resources and take charge of mobilization training of subordinate units, and during wartime, in conjunction with mobilization coordination team and Cell, can ensure the execution of mobilization. Second, Future oriented reserve forces management system such as service system of reserve forces and support system of homeland defense operations. Current service and trainings of reserve forces by the year have very low connection, as it is very complex to manage the resources and trainings, and service and training lack the equity, re-establishment of service system is required. Also in an aspect of CSS and cultivation support for reserve forces, as the scope and limitation of responsibility between the armed forces and autonomous organization is obscure, conditions to conduct actual-fighting exercises are limited. Concentrated budgetting is extremely difficult because reserve forces training fields are scattered nationwide, and facilities and equipments are rapidly getting older. To improve all these, I suggest the organization of homeland defense battalion with a unit of "City-Gun-District" and supporting the local reserve forces. Conduct unit replacement or personal replacement for those who have finished their 1 or 2 years and homeland defense operation duty for those with 3-5 years for consistency and simplification. Third, I suggest Future oriented Reserved Training(FRT) and Training Center oriented training management to establish a reliable reserve training. Reserves carry out expansion of unit, conventional combat mission, homeland defense and logistics support during wartime, and actual-fighting exercise, and disaster relief, peace keeping activities. Despite diverse activities and roles, their training condition still stays definitely poor. For these reasons, Modernization of weapons and facilities through gradual replacement and procurement is essential to enhance mobilization support system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.754-761
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• 2008

As the automobile industry and technologies are developed, driver's tend to more concern about service matters than mechanical matters. For this reason, interests about recognition of human knowledge and emotion to make safe and convenient driving environment for driver are increasing more and more. recognition of human knowledge and emotion are emotion engineering technology which has been studied since the late 1980s to provide people with human-friendly services. Emotion engineering technology analyzes people's emotion through their faces, voices and gestures, so if we use this technology for automobile, we can supply drivels with various kinds of service for each driver's situation and help them drive safely. Furthermore, we can prevent accidents which are caused by careless driving or dozing off while driving by recognizing driver's gestures. the purpose of this paper is to develop a system which can recognize states of driver's emotion and attention for safe driving. First of all, we detect a signals of driver's emotion by using bio-motion signals, sleepiness and attention, and then we build several types of databases. by analyzing this databases, we find some special features about drivers' emotion, sleepiness and attention, and fuse the results through Multi-Modal method so that it is possible to develop the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.327-333
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• 2016

An autonomous vehicle is a convergence of artificial intelligence and a vehicle which can drive itself while analyzing the real-time situation on a road without a driver. A lot of research achievements have been revealed through the media and Google is considered to be the best leading company in this field. The use of patent information which contains various information such as bibliographic data and information about technologies is a good way to find out the R&D direction of a company and develop a reasonable strategy. This study is aimed at investigating the direction to which Google focuses its R&D capabilities and establishing strategies for technology development. Google's patents about autonomous vehicles were collected and the degree of research bias was analyzed using Social Network Analysis based on citations indicating the quality of a patent. Based on the results, the strategies for technology development was eventually proposed. As a result, it was revealed that Google focused its R&D capabilities on the part of hardware control to make up for its lack of hardware-oriented technologies. As of now, Google obtained remarkable achievements, so it seems reasonable that last-movers consider cooperative research with Google.

• Journal of IKEEE
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• v.21 no.4
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• pp.388-396
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• 2017

Since there are many variables such as various poses, illuminations and occlusions in a face detection problem, a high performance detection system is required. Although CNN is excellent in image classification, CNN operatioin requires high-performance hardware resources. But low cost low power environments are essential for small and mobile systems. So in this paper, the CPU-FPGA integrated system is designed based on 3-stage cascade CNN architecture using small size FPGA. Adaptive Region of Interest (ROI) is applied to reduce the number of CNN operations using face information of the previous frame. We use a Field Programmable Gate Array(FPGA) to accelerate the CNN computations. The accelerator reads multiple featuremap at once on the FPGA and performs a Multiply-Accumulate (MAC) operation in parallel for convolution operation. The system is implemented on Altera Cyclone V FPGA in which ARM Cortex A-9 and on-chip SRAM are embedded. The system runs at 30FPS with HD resolution input images. The CPU-FPGA integrated system showed 8.5 times of the power efficiency compared to systems using CPU only.

• Journal of Korea Water Resources Association
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• v.48 no.6
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• pp.425-438
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• 2015

The objective of this study was to evaluate simulation of agricultural water supply considering yearly variation of irrigation efficiency. The water supply data of the Idong reservoir from 2001 through 2009 was collected and used for this study. Total 6 parameters including irrigation efficiency (Es), drainage outlet height, and infiltration, were used for sensitivity analysis, calibration, and validation. Among the parameters, the Es appeared to be the most sensitivity parameter. The Es was calibrated on a yearly basis considering sensitivity and time-varying characteristic, while other parameters were set to fixed values. The statistics of percent bias (PBLAS), Nash-Sutcliffe efficiency (NSE), and root means square error to the standard deviation of measured data (RSR) for a monthly step were 2.7%, 0.93, and 0.26 for the calibration, and 3.9%, 0.89, and 0.32 for the validation, correspondently. The results showed a good agreement with the observations. This implies that the modeling only with appropriate parameter values, apart from modeling approaches, can simulate the real supply operation reasonably well. However, the simulations with uncalibrated parameters from previous studies produced poor results. Thus, it is important to use calibrated values, and especially, we suggest the Es's yearly calibration for simulating agricultural water supply.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.25-31
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• 2014

Bioelectrical Impedance Analysis(BIA) is a widely used method for estimating body composition changes which is a non-invasive, inexpensive, safety and reproductive method. We studied the bioimpedance change and the distinction of blood pressure according to body posture and conducted three kinds of experiments: the real-time bioimpedance measurement, the simulation using equivalent circuit model and the blood pressure measurement. Bioimpedance is measured during 4 minutes at the multi-frequency(1 kHz, 10 kHz, 20 kHz, 50 kHz, 70 kHz, 100 kHz). From the experiment results, the changes in body postures result in changes of resistance and reactance, with an average rapid increase of body impedance when going from standing, sitting to supine. Specially, the laying resistance on average was 16.49% higher than supine resistance at 50 kHz and the laying reactance measurement was also 26.05% higher than sitting reactance at 1 kHz. Blood pressure in standing posture was higher than those in other postures both in maximum($125.14{\pm}12.30$) and in minimum($75.57{\pm}10.31$). The results of BIA and blood pressure in this study will be contributed to the research on acute illness, extreme fat, and body shape abnormalities.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.9-14
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• 2004

Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.749-754
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• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).