Kimpan, Warangkhana

Preferred name

Kimpan, Warangkhana

Alternative Name

Kimpan, W.

Main Affiliation

School of Science

warangkhana.ki@kmitl.ac.th

Now showing 1 - 10 of 13

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Network Architecture of ETAT Education and Training Centers for Automation 4.0
(2022-01-01)
De Marchi, Matteo
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Jitngernmadan, Prajaks
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Singsri, Pongpat
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Putpuek, Narongsak
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Kumpakeaw, Saman
Automation 4.0 comprises the latest technologies that enhance classic automation to communicate with machines in a connected manner and to enable more flexible and more intelligent automation in smart factories. Industry 4.0 has become a global trend over the past ten years to combine the benefits of automation with the needs for flexible production. This trend has reached, in addition to industrialized countries, also newly industrialized countries like Thailand, with the aim to strengthen the global competitiveness. In the ETAT project, partners from Europe as well as Thailand combine their competences to build up a network of so-called Smart Labs around Bangkok area and thus take an important step towards Automation 4.0 in Thailand. Using Thailand as an example, this work shows how industrially emerging countries can sustainably strengthen their qualification with regard to Automation 4.0 by setting up such networks of education and training centers. In addition to explaining the general architecture of the network, the specific characteristics of all six established ETAT Smart Labs are presented and discussed.
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Define Stance and Swing pattern of gait cycle using motion sensor and K-Mean Clustering
(2022-01-01)
Santikan, Piyapon
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Tangwongcharoen, Wisan
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Kimpan, Warangkhana
This research studied walking patterns based on the gait cycle focused on the stance and swing period. We are interested in creating the pattern for representing a normal person and person with a walking disorder by distinguishing patterns. This research uses Razor-IMU to collect all walking data and transfer data from sensors via WIFI which helps gain data to be stable and accurate.After collecting the walking data, we transformed data into linear graphs to reference the gait cycle pattern. Because the graph in linear form can show the movement and distinguish between normal and abnormal people the difference. The aim is to obtain representative data of normal and abnormal people for further analysis. Therefore, the data were then grouped using K-mean Clustering. The data obtained from the clusters were able to distinguish between normal and abnormal walking distances.
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Enhancing of Particle Swarm Optimization Based Method for Multiple Motifs Detection in DNA Sequences Collections
(2020-05-01)
Som-In, Sarawoot
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Kimpan, Warangkhana
Genome sequence data consists of DNA sequences or input sequences. Each one includes nucleotides with chemical structures presented as characters: 'A ' 'C'' G''A','C','G', and 'T', and groups of motif sequences, called Transcription Factor Binding Sites (TFBSs), which are subsequences of DNA that lead to protein-synthesis. The detection of TFBSs is an important problem for bioinformatics research. With the similar patterns of motif sequences in TFBSs, computational algorithms for TFBSs detection have been improved to reduce resources used in laboratory setting. The metaheuristic algorithm is the important issue that has been continually improved to detect TFBSs with greater precision and recall. This paper proposes PSO_HD by applying Particle Swarm Optimization (PSO) as a pre-process and using Hamming distance to improve the efficiency of detecting TFBSs with more precision and recall. In order to measure its efficiency, the paper compares the TFBSs detection using PSO_HD algorithm with relevant algorithms in eight datasets. F-score is used as a measurement unit and compared to the related algorithms. The experimental results show that PSO_HD algorithm gives the highest average F-score, which can be indicated that the PSO_HD algorithm can improve the efficiency of detecting TFBSs with more precision and recall.
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STRAWBERRY SEEDLING CULTIVATION WITH SMART FARM
(2025-01-01)
Kruekaew, Boonhatai
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Palananda, Attapon
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Kimpan, Warangkhana
In Thailand, the main planting areas are in the upper northern region. It is difficult for strawberry to be cultivated in the central region of Thailand due to inappropriate weather conditions. Because the strawberry seedlings are delicate and sensitive to alterations in temperature and weather, they require extra care than mature plants while cultivation before planting. Therefore, modifications to strawberry seedling cultivation may result in more strawberry plantings. Consequently, one of the most beneficial choices is a smart farming. This research focuses on the cultivation of strawberry seedlings in tropical areas of Thailand using an intelligence model and Internet of Things. The prototype system focuses on automatic watering and lighting, and an environmental adaptation system that combines sensors to control water, air, and lighting. The physical characteristics data from all devices in prototype system are collected, and then analyzed using machine learning methods to automatically control the environment within the prototype system to be suitable for growing strawberry seedlings. Moreover, the real-time data will be displayed on a dashboard with various notification systems. The experimental results indicated that using machine learning models can control the suitable temperature and humidity for strawberry seedlings cultivation. The appropriate temperature and soil moisture are between 31-32 degrees Celsius and 70 percent, respectively.
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Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
(2021-04-23)
Puraram, Tanakorn
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Chaovalit, Pimwadee
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Peethong, Apatha
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Tiyanunti, Pongsak
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Charoensiriwath, Supiya
A food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
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Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
(2020-08-01)
Kimpan, Warangkhana
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Kasetvetin, Sirawich
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Kimpan, Chom
The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.
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Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
(2025-01-01)
Teerasarn, Chalanwich
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Kimpan, Warangkhana
This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
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Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm with Reinforcement Learning
(2022-01-01)
Kruekaew, Boonhatai
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Kimpan, Warangkhana
Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC-LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.
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WILDFIRE CLASSIFICATION WITH DEEP LEARNING MODEL
(2026-07-01)
Tankue, Puwanai
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Kruekaew, Boonhatai
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Kimpan, Warangkhana
This research aims to develop models for wildfire classification from images. The objective is to provide decision-making support for wildfire control planning, prevention and management. Convolutional Neural Networks techniques are used to analyze a dataset consisting of three image groups: no fire images, fire images that are not wildfires, and wildfire images. The experimental results compared ResNet group, DenseNet group, MobileNet group, and EfficientNet group. The research findings indicate the best-performing model in this study is ResNet152V2, which achieved an accuracy of 92.75%. Furthermore, Precision, Recall, and F1-Score are within a satisfactory range. A web application has also been developed to facilitate users to detect and classify wildfire more conveniently.
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Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing
(2020-01-01)
Kruekaew, Boonhatai
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Kimpan, Warangkhana
This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.

Kimpan, Warangkhana

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