Nowadays, automobiles are the most widely used form of transportation. This increases the risk of accidents. Therefore, car users prefer to get insurance to reduce the risk in the event of an accident. As for the insurance company, the company will be responsible for damages according to the conditions of the policy. One of the duties of a company's claims department is to procure spare parts to control costs. However, in the case of compensation, there may be erroneous operations, such as ordering the wrong parts or ordering more than necessary. Currently, insurance companies do not have a very efficient management system. This research aims to develop a system for managing and storing automobile parts for insurance companies. The system is designed to be able to track the status of spare parts from storage to disbursement. It uses barcode technology to increase accuracy and reduce errors in data recording. Such a system will help insurance companies manage spare parts systematically, reduce unnecessary costs, and increase efficiency in providing services.
การประกันภัยเป็นที่นิยมมากในปัจจุบัน บริษัทประกันจึงจำเป็นที่จะต้องปรับตัวรับมือกับกรมธรรม์ที่มากขึ้นในทุกๆปี ทำให้จะต้องมีระบบในการรองรับและจัดการต่างๆ ซึ่งในหน่วยงานของสินไหมรถยนต์เอง ก็ต้องมีระบบและการจัดการที่มีประสิทธิภาพเช่นกัน และหนึ่งในนั้นคืองานระบบภายในและระบบภายนอกที่เกิดจากความต้องการของสาขาต่างๆของบริษัท และคู่ค้าสัญญาในเครือ ซึ่งทำให้เกิดระบบขึ้นมา ได้แก่ ระบบจัดการและจัดเก็บอะไหล่
คณะเทคโนโลยีสารสนเทศ
Facial Expression Recognition (FER) has attracted considerable attention in fields such as healthcare, customer service, and behavior analysis. However, challenges remain in developing a robust system capable of adapting to various environments and dynamic situations. In this study, the researchers introduced an Ensemble Learning approach to merge outputs from multiple models trained in specific conditions, allowing the system to retain old information while efficiently learning new data. This technique is advantageous in terms of training time and resource usage, as it reduces the need to retrain a new model entirely when faced with new conditions. Instead, new specialized models can be added to the Ensemble system with minimal resource requirements. The study explores two main approaches to Ensemble Learning: averaging outputs from dedicated models trained under specific scenarios and using Mixture of Experts (MoE), a technique that combines multiple models each specialized in different situations. Experimental results showed that Mixture of Experts (MoE) performs more effectively than the Averaging Ensemble method for emotion classification in all scenarios. The MoE system achieved an average accuracy of 84.41% on the CK+ dataset, 54.20% on Oulu-CASIA, and 61.66% on RAVDESS, surpassing the 71.64%, 44.99%, and 57.60% achieved by Averaging Ensemble in these datasets, respectively. These results demonstrate MoE’s ability to accurately select the model specialized for each specific scenario, enhancing the system’s capacity to handle more complex environments.
คณะเทคโนโลยีการเกษตร
The Public park project : Ancient Sea Park. This's a new park in Aangsila Chonburi make for learn and travel in concept The sea in 65 million years ago.
คณะวิทยาศาสตร์
In this paper, Vanadium dioxide (VO2) thin-film devices with two different use cases have been redesigned to introduce an asymmetrical resonant cavity structure. The structure is designed with the goal of enhancing the optical performance of the central VO2 layer and has an anti-reflection property in the cold state. The advantages and limitations of such a design are discussed.