Cheju Halla University

About: Cheju Halla University is a education organization based out in Jeju City, South Korea. It is known for research contribution in the topics: Supply chain & Nurse education. The organization has 97 authors who have published 156 publications receiving 1186 citations.

Real-time illumination invariant lane detection for lane departure warning system

Abstract: Invariant property of lane color under various illuminations is utilized for lane detection.Computational complexity is reduced using vanishing point detection and adaptive ROI.Datasets for evaluation include various environments from several devices.Simulation demo demonstrate fast and powerful performance for real-time applications. Lane detection is an important element in improving driving safety. In this paper, we propose a real-time and illumination invariant lane detection method for lane departure warning system. The proposed method works well in various illumination conditions such as in bad weather conditions and at night time. It includes three major components: First, we detect a vanishing point based on a voting map and define an adaptive region of interest (ROI) to reduce computational complexity. Second, we utilize the distinct property of lane colors to achieve illumination invariant lane marker candidate detection. Finally, we find the main lane using a clustering method from the lane marker candidates. In case of lane departure situation, our system sends driver alarm signal. Experimental results show satisfactory performance with an average detection rate of 93% under various illumination conditions. Moreover, the overall process takes only 33ms per frame.

Face liveness detection using variable focusing

Abstract: As Face Recognition(FR) technology becomes more mature and commercially available in the market, many different anti-spoofing techniques have been recently developed to enhance the security, reliability, and effectiveness of FR systems. As a part of anti-spoofing techniques, face liveness detection plays an important role to make FR systems be more secured from various attacks. In this paper, we propose a novel method for face liveness detection by using focus, which is one of camera functions. In order to identify fake faces (e.g. 2D pictures), our approach utilizes the variation of pixel values by focusing between two images sequentially taken in different focuses. The experimental result shows that our focus-based approach is a new method that can significantly increase the level of difficulty of spoof attacks, which is a way to improve the security of FR systems. The performance is evaluated and the proposed method achieves 100% fake detection in a given DoF(Depth of Field).

A Multi-Objective Optimization of Energy, Economic, and Carbon Emission in a Production Model under Sustainable Supply Chain Management

Abstract: Nowadays, many industries are focusing on automation in manufacturing for high production and good quality to meet the needs of customers in a short period of time. This trend has produced a forward shift in technology in the form of advancement, which ultimately increases energy demand. For that reason, researchers have started working on sustainable development associated with cleaner-energy policies to avoid increasing energy consumption for enhanced manufacturing technology in developed countries. The other important issue affecting our world is global warming, which is the result of greenhouse gas emissions. That is the reason, renewable energies like solar energy have dramatically increased during recent years to compensate for the energy demand and reduced carbon footprint for cleaner production. This paper considers a supply chain management of automobile part manufacturing industry with suppliers to optimize the production quantity with multiple objectives i.e., minimizing the total cost of production including minimum quantity lubrication is a first objective, reduction of the carbon footprint is the second, and minimizing the cost of energy considering renewable energy is the last objective. This study considers a situation, where imperfect quality items are managed and controlled by the suppliers as outsourcing operations. A weighted goal programming methodology is utilized to solve the proposed mathematical model including sustainable suppliers. Sensitivity analysis of the model is performed for different scenarios with respect to the energy utilization. The optimal result of minimum production cost and carbon emissions is the evidence of successful pragmatic application in automobile industry. The results validate the model to provide the basis for sustainability in supply chain environment considering manufacturer and suppliers.

Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database

Abstract: The subjective nature of pain makes it a very challenging phenomenon to assess. Most of the current pain assessment approaches rely on an individual’s ability to recognise and report an observed pain episode. However, pain perception and expression are affected by numerous factors ranging from personality traits to physical and psychological health state. Hence, several approaches have been proposed for the automatic recognition of pain intensity, based on measurable physiological and audiovisual parameters. In the current paper, an assessment of several fusion architectures for the development of a multi-modal pain intensity classification system is performed. The contribution of the presented work is two-fold: (1) 3 distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation. (2) An extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system. The assessment is based on the SenseEmotion Database and experimental validation demonstrates the relevance of the multi-modal classification approach, which achieves classification rates of respectively $83.39\%$ 83 . 39 % , $59.53\%$ 59 . 53 % and $43.89\%$ 43 . 89 % in a 2-class, 3-class and 4-class pain intensity classification task.