• Journal of Korean Medicine Rehabilitation
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• v.20 no.2
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• pp.89-99
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• 2010

Objectives : This study was designed to analyze the pattern of asymmetrical alignment. Methods : This study was carried out with the data from comprehensive medical testing. 91 subjects aged 30-39 were evaluated by full spine AP X-ray. For pelvis, innominate measurement(IM), off centering measurement(OCM), ilium shadow measurement(ISM), major axis of obturator foramen(MaF), minor axis of obturator foramen(MiF) were analyzed. Spinal curvature and height of shoulder girdle were analyzed. Results : 1. In pelvis, It. posterior-inferior and it. inflare combination pattern was 38 cases(42.8%). 2. In spinal curvature, "reverse S" curve was 45 cases(49.4%) and "reverse C" curve was 30 cases(33%). 3. In shoulder girdle, It. superior pattern was 42 cases(46.1 %) and It. superior pattern was 39 cases(42.9%). 4. In whole body analysis, It. posterior-inferior and It. inflare pelvis, "reverse S" spinal curvature and It. superior shoulder girdle combination patten was 11 cases(12.1 %). This pattern is similar to Kendall's right handedness pattern and Zink's common compensatory pattern. Conclusions : Results from this investigation showed asymmetrical alignment in 30-39 years-old adults. This results are expected to contribute to classifying the alignment pattern in clinic and systemic treatment.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.51-57
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• 2017

Digital signage is becoming part of daily life across a wide range of visual advertisements segments market used in stations, hotels, retail stores, hotels, etc. The current digital signage system used in market is generally works on limited user interactivity with static contents. In this paper, a new approach is proposed using computer vision based dynamic audience adaptive cost-effective digital signage system. The proposed design uses the Camera attached Raspberry Pi Open source platform to employ the real-time audience interaction using computer vision algorithms to extract facial features of the audience. The real-time facial features are extracted using Haar Cascade algorithm which are used for audience gender specific rendering of dynamic digital signage content. The audience facial characterization using Haar Cascade is evaluated on the FERET database with 95% accuracy for gender classification. The proposed system, developed and evaluated with male and female audiences in real-life environments camera embedded raspberry pi with good level of accuracy.

• Journal of Navigation and Port Research
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• v.34 no.5
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• pp.343-348
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• 2010

As the marine transportation has increased, the safe maneuvering system has been required. SOLAS obligated regulations for carrying AIS(Automatic Identification System) onboard. This system provides navigational information including identification, classification, position, a ship's course and voyage status from own vessel to ground and other vessels. Being utilized in the most development of application service for safe maneuvering, they are to be managed systematic. In this paper, we introduce a database system to manage the voyage status and navigational information of AIS message. It is classified by static or dynamic. An electronic navigational chart display system is implemented to verify the design.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.51-56
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• 2009

We developed an augmented new reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.125-160
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• 2007

Data Warehousing and OLAP (On-Line Analytical Processing) have turned into the key technology for comprehensive data analysis. Originally developed for the needs of decision support in business, data warehouses have proven to be an adequate solution for a variety of non-business applications and domains, such as government, research, and medicine. Analytical power of the OLAP technology comes from its underlying multidimensional data model, which allows users to see data from different perspectives. However, this model displays a number of deficiencies when applied to non-conventional scenarios and analysis tasks. This paper presents an attempt to systematically summarize various extensions of the original multidimensional data model that have been proposed by researchers and practitioners in the recent years. Presented concepts are arranged into a formal classification consisting of fact types, factual and fact-dimensional relationships, and dimension types, supplied with explanatory examples from real-world usage scenarios. Both the static elements of the model, such as types of fact and dimension hierarchy schemes, and dynamic features, such as support for advanced operators and derived elements. We also propose a semantically rich graphical notation called X-DFM that extends the popular Dimensional Fact Model by refining and modifying the set of constructs as to make it coherent with the formal model. An evaluation of our framework against a set of common modeling requirements summarizes the contribution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1348-1353
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• 2007

We developed an augmented new reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.337-351
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• 2021

Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Steel and Composite Structures
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• v.49 no.6
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• pp.685-699
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• 2023

In recent years, Carbon Fibre Reinforced Plastic (CFRP) strengthening is found to be one of the best methods to strengthen steel structures. The fibrous bond can also influence the vibration characteristics of the strengthened element apart from its static strength enhancement property. The main objective of this study is to understand the influence of CFRP strengthening on the dynamic Behaviour of Thin-Webbed Castellated Beams (TWCBs). A detailed experimental investigation was carried out on five sets of beams with varying parameters such as domination of shear (Shear Dominant, Moment Dominant and Moment and Shear Dominant), sectional classification (Plastic and Semi-compact) and perforation geometries (h_o/d_wratio 0.65 and e/h_o ratio 0.3). Free vibration analysis was carried out by exciting the simply supported TWCBs with an impact force generated by a ball dropped from a specific height. Logarithmic decrement method was used to obtain the damping ratio and natural frequencies of vibration were found by Fast Fourier Transform (FFT). Natural frequency showed an increase in a range of 10.5 - 55% for the different sets of castellated beams. An increase of 62.30% was noted in the damping ratio of TWCBs after strengthening which is an indication of improvement in the vibration characteristics of the beam.

• Journal of Distribution Science
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• v.22 no.9
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• pp.85-95
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• 2024

Purpose: Online food delivery platforms face challenges to operational efficiency due to increasing demand, a shortage of drivers, and the constraint of a one-order-at-a-time delivery policy. It is imperative to find solutions to address the inefficiencies in the food delivery industry. Bundling multiple orders can help resolve these issues, but it requires complex computations due to the exponential increase in possible order combinations. Research design, data and methodology: This study proposes three bundle delivery systems-static, dynamic, and hybrid-utilizing a machine learning-based classification model to reduce the number of order combinations for efficient bundle computation. The proposed systems are analyzed through simulations using market data from South Korea's online food delivery platforms. Results: Our findings indicate that implementing bundle systems extends service coverage to more customers, increases average driver earnings, and maintains lead times comparable to standalone deliveries. Additionally, the platform experiences higher service completion rates and increased profitability. Conclusions: This suggests that bundle systems are cost-effective and beneficial for all stakeholders in online food delivery platforms, effectively addressing the inefficiencies in the industry.