• Journal of Integrative Natural Science
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• v.3 no.1
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• pp.49-53
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• 2010

Molecular docking is a critical event which mostly forms Van der waals complex in molecular recognition. Since the majority of developed drugs are small molecules, docking them into proteins has been a prime concern in drug discovery community. Since the binding pose space is too vast to cover completely, many search algorithms such as genetic algorithm, Monte Carlo, simulated annealing, distance geometry have been developed. Proper evaluation of the quality of binding is an essential problem. Scoring functions derived from force fields handle the ligand binding prediction with the use of potential energies and sometimes in combination with solvation and entropy contributions. Knowledge-based scoring functions are based on atom pair potentials derived from structural databases. Forces and potentials are collected from known protein-ligand complexes to get a score for their binding affinities (e.g. PME). Empirical scoring functions are derived from training sets of protein-ligand complexes with determined affinity data. Because non of any single scoring function performs generally better than others, some other approaches have been tried. Although numerous scoring functions have been developed to locate the correct binding poses, it still remains a major hurdle to derive an accurate scoring function for general targets. Recently, consensus scoring functions and target specific scoring functions have been studied to overcome the current limitations.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.883-886
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• 2004

Many of facial feature extracting applications and systems have been developed in the field of face recognition systems and its application, and most of them use the eyes as a key-feature of human face. In this paper we show a simple and fast eye detection algorithm for embedded systems. The eyes are very important facial features because of the attribution they have. For example, we know the darkest regions in a face are the pair of pupils, and the eyes are always a pair and parallel. Using such attributors, our algorithm works well under various light conditions, size of face in image, and various pose such as panning and tilting. The main keys to develop this algorithm are the eyes' attribution that we can usually contemplate and easily find when we think about what is the attribution that the eyes have. With some constraints of the eyes and knowledge of the anthropometric human face, we detect human eye in an image, and the experimental results demonstrate successful eye detection.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.876-892
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• 2017

A virtual reality is a virtual space constructed by a computer that provides users the opportunity to indirectly experience a situation they have not experienced in real life through the realization of information for virtual environments. Various studies have been conducted to realize virtual reality, in which the user interface is a major factor in maximizing the sense of immersion and usability. However, most existing methods have disadvantages, such as costliness or being limited to the physical activity of the user due to the use of special devices attached to the user's body. This paper proposes a new type of interface that enables the user to apply their intentions and actions to the virtual space directly without special devices, and test content is introduced using the new system. Users can interact with the virtual space by throwing an object in the space; to do this, moving object detectors are produced using infrared sensors. In addition, the users can control the virtual space with their own postures. The method can heighten interest and concentration, increasing the sense of reality and immersion and maximizing user's physical experiences.

• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.17-24
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• 2023

With the continuous growth in the amount of data collected and analyzed, deep learning has become increasingly popular for extracting meaningful insights from various fields. However, hardware limitations pose a challenge for achieving meaningful results with limited data. To address this challenge, this paper proposes an algorithm that leverages the characteristics of convolutional neural networks (CNNs) to reduce the size of image datasets by 20% through smoothing and shrinking the size of images using color elements. The proposed algorithm reduces the learning time and, as a result, the computational load on hardware. The experiments conducted in this study show that the proposed method achieves effective learning with similar or slightly higher accuracy than the original dataset while reducing computational and time costs. This color-centric dataset construction method using image smoothing techniques can lead to more efficient learning on CNNs. This method can be applied in various applications, such as image classification and recognition, and can contribute to more efficient and cost-effective deep learning. This paper presents a promising approach to reducing the computational load and time costs associated with deep learning and provides meaningful results with limited data, enabling them to apply deep learning to a broader range of applications.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.1-5
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• 2023

With recent advancements in artificial intelligence (AI) technology, more products based on AI are being launched and used. However, using AI safely requires an awareness of the potential risks it can pose. These concerns must be evaluated by experts and users must be informed of the results. In response to this need, many countries have implemented certification programs for products based on AI. In this study, we analyze several trends and differences in AI product certification programs across several countries and emphasize the importance of such programs in ensuring the safety and trustworthiness of products that include AI. To this end, we examine four international AI product certification programs and suggest methods for improving and promoting these programs. The certification programs target AI products produced for specific purposes such as autonomous intelligence systems and facial recognition technology, or extend a conventional software quality certification based on the ISO/IEC 25000 standard. The results of our analysis show that companies aim to strategically differentiate their products in the market by ensuring the quality and trustworthiness of AI technologies. Additionally, we propose methods to improve and promote the certification programs based on the results. These findings provide new knowledge and insights that contribute to the development of AI-based product certification programs.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.613-618
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• 2023

In this paper, a monitoring program that can automatically determine whether a patient admitted to an isolation room acts out of a stable state through a screen photographed in real time is described. The motion recognition model of this program was built by learning through transfer learning. 900 images were used for the three movements, and this program was developed for the web to support all environments. The model was determined with high accuracy to determine the state of the subject hospitalized in the isolation room, and can be applied by applying it to the existing isolation room monitoring system.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.375-386
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• 2024

Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.77-84
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• 2012

Face classification has wide applications in intelligent video surveillance, content retrieval, robot vision, and human-machine interface. Pose and expression changes, and arbitrary illumination are typical problems for face recognition. When the face is captured at a distance, the image quality is often degraded by blurring and noise corruption. This paper investigates the efficacy of multi-classifier decision level fusion for face classification based on the photon-counting linear discriminant analysis with two different cost functions: Euclidean distance and negative normalized correlation. Decision level fusion comprises three stages: cost normalization, cost validation, and fusion rules. First, the costs are normalized into the uniform range and then, candidate costs are selected during validation. Three fusion rules are employed: minimum, average, and majority-voting rules. In the experiments, unfocusing and motion blurs are rendered to simulate the effects of the long distance environments. It will be shown that the decision-level fusion scheme provides better results than the single classifier.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.112-120
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• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.