A Photographic series that expresses the abstract states of myself, towards the question of existence that results from being surrounded by expectations of both surrender and freedom of expression, this series focuses on my own subjectivities in order to bring back memories of almost forgotten feelings and make them clear once more.
การที่เติบโตมาจากครอบครัวที่คาดหวังในตัวเรา ที่สมาชิกคาดหวังในตัวเราไม่เหมือนกัน ถ้าเราทำแบบใดแบบหนึ่งที่คนใดคนหนึ่งต้องการอีกคนจะไม่พอใจ จนเราเกิดสงสัยว่าเราต้องเป็นแบบไหน เมื่อเข้ามาอยู่ในสังคมใหม่ทำให้เราตั้งคำถามกับตนเองเมื่อเข้าหาผู้คนว่าเราต้องเป็นไปแบบที่เขาต้องการหรือเปล่าเราถึงจะเข้าถึงเขาได้ ทำให้เราสับสนกับตัวเองและต้องสร้างตัวตนใหม่ไปตามที่คนคนนั้นพอใจ จนเราเองเริ่มเกิดคำถามว่าจริงๆแล้วตัวตนของเราจริงๆเป็นแบบไหน
คณะวิศวกรรมศาสตร์
This research aims to reduce the time required to resolve customer issues by focusing on improvements based on lean principles and the application of technology. The researcher conducts the case study at Nexter Digital and Solution Co., Ltd. to enhance workflows, establish new work standards, and integrate Bot technology into the processes to reduce resolution time and set new performance benchmarks for the company. The research proposes key ideas, such as identifying the root cause of problems, reducing redundant processes, implementing Lean methodologies, and applying technology to streamline operations. The research identifies two main issues to be resolved. The first involves addressing customer complaints, where the results show that the average resolution time reduces from 5 days to 3 days, representing a 38% decrease. The second issue involves solving problems for vendors, where the results show that the average response time reduces from 20 minutes to within 1 minute, a 98.5% decrease. The findings from both cases not only improve customer service but also establish a new standard for responding to and resolving internal issues more efficiently.
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
The objective of this research is to utilize waste slag in industrial applications and help mitigate flooding, water accumulation, and ponding issues. Currently, slag from the steel smelting or refining process is commonly used as a component in construction materials, such as road surfaces. However, slag has properties that make it difficult for water to permeate, leading to poor drainage and increased flooding problems. This study focuses on improving the properties of pavement materials to enhance their strength and water permeability. This can be achieved through physical structural modifications or the addition of chemical agents such as HPMC, which increases void spaces to facilitate water absorption and drainage according to required standards. The utilization of waste slag not only helps reduce production costs and improve material performance but also minimizes environmental impacts and promotes the sustainable use of resources.
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.