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Hydro IoT

Hydro IoT

Abstract

A small hydroponic vegetable growing system simulation kit with water flow system that monitors, maintains and controls the amount of fertilizer in the system.

Objective

ในปัจจุบันผู้คนหันมาสนใจการปลูกผักไฮโดรโปนิกส์เพื่อสำหรับการบริโภคภายในครัวเรือนมากขึ้น และด้วยวิถีชีวิตในปัจจุบันที่มีพื้นที่จำกัดและมีเวลาที่จำกัด การดูแลและติดตั้งระบบปลูกผักไฮโดรโปนิกส์จึงยังมีข้อจำกัดเหล่านี้ การมีตัวช่วยในการดูแลผักและใช้พื้นที่ขนาดเล็กจะช่วยตอบโจทย์ให้กับวิถีชีวิตของคนในปัจจุบัน ที่มีเวลาดูแลฟาร์มและพื้นที่ที่จำกัด

Other Innovations

Detection of Storage Age Adulteration in Khao Dawk Mali 105 Rice  using Near-Infrared Spectroscopy

คณะวิศวกรรมศาสตร์

Detection of Storage Age Adulteration in Khao Dawk Mali 105 Rice using Near-Infrared Spectroscopy

This research aims to investigate the adulteration of Khao Dawk Mali 105 rice based on storage age using Near-Infrared Spectroscopy (NIRS) with Fourier Transform Near-Infrared Spectroscopy (FT-NIR) in the wavenumber range of 12,500 – 4,000 cm-1 (800 – 2,500 nm). Storage duration significantly impacts the quality of cooked rice. This research is divided into two parts: 1) to investigate the feasibility of separating rice according to storage age (1, 2, and 3 years) using the best model created by an Ensemble method combined with Second Derivative, which achieved an accuracy of 96.3%. 2) To investigate adulteration based on storage age by adulterating at 0% (all 2- and 3-year-old rice), 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% (all 1-year-old rice). The best model was created using Gaussian Process Regression (GPR) combined with Smoothing + Multiplicative Scatter Correction (MSC), with coefficients of determination (r²), root mean square error of prediction (RMSEP), bias, and prediction ability (RPD) values of 0.92, 8.6%, 0.9%, and 3.6 respectively. This demonstrates that the adulteration model can be applied to separate rice by storage age (1, 2, and 3 years). Additionally, the color values of rice with different storage ages show differences in L* and b* values.

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The Application of AI Chatbots and Lean Principles to Reduce Waiting Time for Customers and Vendors to Enhance Service Quality

คณะวิศวกรรมศาสตร์

The Application of AI Chatbots and Lean Principles to Reduce Waiting Time for Customers and Vendors to Enhance Service Quality

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.

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