• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.557-562
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• 1999

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.691-697
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• 1999

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.691-692
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.441-442
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• 2007

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.02a
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• pp.587-593
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• 2000

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.423-424
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• 2011

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.256-266
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• 2024

Sasang constitutional medicine (SCM) is one of the best traditional therapeutic approaches used in Korea. SCM prioritizes personalized treatment that considers the unique constitution of an individual and encompasses their physical characteristics, personality traits, and susceptibility to specific diseases. Facial features are essential for diagnosing Sasang constitutional types (SCTs). This study aimed to develop a real-time artificial intelligence-based model for diagnosing SCTs using facial images, building an SCTs prediction model based on a machine learning method. Facial features from all images were extracted to develop this model using feature engineering and machine learning techniques. The fusion of these features was used to train the AI model. We used four machine learning algorithms, namely, random forest (RF), multilayer perceptron (MLP), gradient boosting machine (GBM), and extreme gradient boosting (XGB), to investigate SCTs. The GBM outperformed all the other models. The highest accuracy achieved in the experiment was 81%, indicating the robustness of the proposed model and suitability for real-time applications.

• ETRI Journal
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• v.36 no.3
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• pp.439-449
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• 2014

In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1858-1861
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• 2005

Automatic nut sorting machine used to calculate the size of inserted nut and detect the black spot defection is introduced in this paper. Because most of farm products are imported from the underdeveloped countries, domestic farm products have no place to be sold in market. To overcome this critical situation, lowering the productivity cost is strongly demanded to compete with foreign corps. Imaged processed nut sorting algorithm is developed to the automatic nut sorting machine to remove the sorting time which takes lots of man power. This system is composed of mainly two parts, mechanical parts and vision system. The purpose of mechanical part is supplying the nuts automatically to make computer system capture the images of objects. Simplified mechanical system was assembled followed by 3D simulation by Pro/E design for the adaptive cost effects. Several image processing algorithms are designed to detect the spot defects and calculate the size of nuts. Test algorithm shows good results to the designed automatic nut sorting system.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.