Sangpetch, Akkarit

Preferred name

Sangpetch, Akkarit

Main Affiliation

School of Engineering

akkarit.sa@kmitl.ac.th

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Structural vibration sensing to evaluate animal activity on a pig farm
(2018-11-04)
Bonde, Amelie
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Sangpetch, Akkarit
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Pan, Shijia
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Woramontri, Woranun
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Zhang, Pei
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RDI: Real digital identity based on decentralized pki
(2018-07-02)
Boontaetae, Pongpayak
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Sangpetch, Akkarit
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Sangpetch, Orathai
Establishing a digital identity plays a vital part in the digital era. It is crucial to authenticate and identify the users in order to perform online transactions securely. For example, internet banking applications normally require a user to present a digital identity, e.g., username and password, to allow users to perform online transactions. However, the username-password approach has several downsides, e.g., susceptible to the brute-force attack. Public key binding using Certificate Authority (CA) is another common alternative to provide digital identity. Yet, the public key approach has a serious drawback: all CAs in the browser/OS' CA list are treated equally, and consequently, all trusts on the certificates could be invalidated by compromising only a single root CA's private key.We propose a Real Digital Identity based approach, or RDI, on decentralized PKI scheme. The core idea relies on a combination of well-known parties (e.g., a bank, a government agency) to certify the identity, instead of relying on a single CA. These parties, collectively known as Trusted Source Certificate Authorities (TSCA), formed a network of CAs. The generated certificates are stored in the blockchain controlled by smart contract. RDI creates a digital identity that can be trusted based on the TSCAs' challenge/response and it is also robust against a single point of trust attack on traditional CAs.
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MassHog: Weight-Sensitive Occupant Monitoring for Pig Pens using Actuated Structural Vibrations
(2021-09-24)
Codling, Jesse R.
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Bonde, Amelie
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Dong, Yiwen
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Cao, Siyi
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Sangpetch, Akkarit
In the swine livestock industry, weight tracking is commonly used to track health and growth of pigs. This is especially crucial during the farrowing period, where mother sows and their newborn piglets are housed together to facilitate nursing. Existing weight measurement methods, however, either provide only sporadic snapshots or have scalability and reliability issues in the harsh environment of industrial farms. This paper presents MassHog, a multi-layered system for ubiquitous weight measurement in harsh environments, such as pig pens, using structural vibrations. MassHog combines an existing sensor concept with actuated vibrations to weigh both piglets and adult hogs together. We evaluate the components of this system through testing deployments, including at real-world operating pig farms. Preliminary results show less than 2 kg mean absolute error for adult hogs.
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Thoth: Automatic resource management with machine learning for container-based cloud platform
(2017-01-01)
Sangpetch, Akkarit
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Sangpetch, Orathai
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Juangmarisakul, Nut
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Warodom, Supakorn
Platform-As-A-Service (PaaS) providers often encounter fluctuation in computing resource usage due to workload changes, resulting in performance degradation. To maintain acceptable service quality, providers may need to manually adjust resource allocation according to workload dynamics. Unfortunately, this approach will not scale well as the number of applications grows. We thus propose Thoth, a dynamic resource management system for PaaS using Docker container technology. Thoth automatically monitors resource usage and dynamically adjusts appropriate amount of resources for each application. To implement the automatic-scaling algorithm, we select three algorithms, namely Neural Network, Q-Learning and our rule-based algorithm, to study and evaluate. The experimental results suggest that Q-Learning can the best adapt to the load changes, followed by a rule-based algorithm and NN. With Q-Learning, Thoth can save computing resources by 28.95% and 21.92%, compared to Neural Network and the rule-based algorithm respectively, without compromising service quality.
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PEX: Privacy-Preserved, Multi-Tier Exchange Framework for Cross Platform Virtual Assets Trading
(2020-01-01)
Sangpetch, Akkarit
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Sangpetch, Orathai
In traditional virtual asset trading market, several risks, e.g. scams, cheating users, and market reach, have been pushed to users (sellers/buyers). Users need to decide who to trust; otherwise, no business. This fact impedes the growth of virtual asset trading market. In the past few years, several virtual asset marketplaces have embraced blockchain and smart contract technology to alleviate such risks, while trying to address privacy and scalability issues. To attain both speed and non-repudiation property for all transactions, existing blockchain-based exchange systems still cannot fully accomplish. In real-life trading, users use traditional contract to provide non-repudiation to achieve accountability in all committed transactions, so-called thorough non-repudiation. This is essential when dispute happens. To achieve similar thorough non-repudiation as well as privacy and scalability, we propose PEX, Privacy-preserved, multi-tier EXchange framework for cross platform virtual assets trading. PEX creates a smart contract for each virtual asset trading request. The key to address the challenges is to devise two-level distributed ledgers with two different types of quorums where one is for public knowledge in a global ledger and the other is for confidential information in a private ledger. A private quorum is formed to process individual smart contract and record the transactions in a private distributed ledger in order to maintain privacy. Smart contract execution checkpoints will be continuously written in a global ledger to strengthen thorough non-repudiation. PEX smart contract can be executed in parallel to promote scalability. PEX is also equipped with our reputation-based network to track contribution and discourage malicious behavior nodes or users, building healthy virtual asset ecosystem.
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GaussianSlicer: Efficient Surface Reconstruction from Cross-sectional Slices with Gaussian Splatting
(2025-01-01)
Guo, Yuhu
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Qian, Chenghao
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Mo, Yuhong
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Sangpetch, Akkarit
In this work, we present GaussianSlicer, an efficient plane-based Gaussian Splatting framework for reconstructing geometry from cross-sectional slices input. Unlike previous methods that rely on computationally intensive geometry-based or grid-based implicit techniques, which struggle with complex cases (e.g., sparse slices, multi-hole geometries). GaussianSlicer enables parallel optimization without requiring any prior constraints. Our method begins by initializing planar Gaussians on each slice and optimizing their layout to obtain accurate geometry representation. To align the Gaussian splats, we introduce a geometric regularization that promotes surface smoothness and ensures consistency in global topology. Our system enables accurate 3D reconstruction from sparse, irregular, multi-label slices with high computational efficiency. Experimental results show that, on average, our method is 45.62% faster and achieves a 21.91% improvement in Chamfer Distance (CD) outperforming state-of-the-art methods on the collected dataset.
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Automated attribute inference for IOT data visualization service
(2019-01-01)
Sangpetch, Orathai
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Sangpetch, Akkarit
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Nartnorakij, Jittinat
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Vejprasitthikul, Narawan
As data becomes vital to urban development of modern cities, Thailand has initiated a smart city project on pilot cities around the country. We have implemented an interoperable data platform for smart city to enable Internet of Things (IoT) data exchanges among organizations through APIs. One of the key success is that people can access and visual the data. However, data can have various attributes since standard has not completely established and adopted. Therefore, it is difficult to automate the process to achieve comprehensive visualization. Traditionally, we require developers to manually examine data streams to determine which data attribute should be presented. This process can be very time consuming. The visualization system must be manually updated whenever a source stream modifies its data attributes. This problem becomes an impediment to implement a scalable cloud-based visualization service. To mitigate this challenge, we propose an automated attribute inference approach to automatically select key visualizable attribute from heterogeneous streams of data sources. We have experimented with different data attribute selection algorithms, namely an empirical rule-based system and the chosen machine learning algorithms. We implement and evaluate the proposed selection algorithms through our 3D visualization program in order to get the feedback from users.
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DASSL: Dynamic, AI-assisted, Scalable System for Labelling Used Bottle Images
(2020-09-21)
Daengphruan, Parnmet
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Sangpetch, Orathai
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Sangpetch, Akkarit
To ensure sustainable consumption and production, one way is to reduce waste generation by increasing the reuse rate. We have been working with the bottle classification facility to enhance the efficiency and productivity. Many used bottles come in with unimaginable ways of dirty, defective conditions. To manage the sheer volume of used bottles, we create an AI-enabled, bottle classification system. However, it requires many labelled images for training to improve accuracy. Unfortunately, the traditional approach, having human label individual images, is very time consuming. Even worse, it is not effective for our dataset because conditions of used bottles are not well defined and studied. From our experiments, the human experts cannot agree on the same labelling for similar bottle conditions, especially when impurities or defects are not separable objects. For 42%-99% of images in certain subcategories, human experts assign different labels to bottles with similar conditions. With huge inconsistency in data labelling, it deteriorates the accuracy of our classification models. To alleviate this problem, we propose a Dynamic, AI-assisted, Scalable System for Labelling used bottle images, called DASSL. DASSL employs multiple algorithms to extract and/or quantize different features of used bottle images, and cluster the images into groups with the supervision of human. With DASSL, we can achieve labelling consistency and improve scalability by reducing the data labelling time by at least 10x. To enhance agility, we can dynamically adjust DASSL to adapt to changes of cleaning machines' capabilities or bottle demand.
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Security context framework for distributed healthcare IoT platform
(2016-01-01)
Sangpetch, Orathai
;
Sangpetch, Akkarit
As Internet of Things (IoT) is entering mainstream, data privacy and security in information exchange becomes a major concern and a barrier for potential adopters, especially in healthcare regime. Information from health IoT devices and services is sensitive and confidential. While many existing works have proposed enhancements and security prospects for individual devices and components in IoT ecosystems, they still do not address the underlying challenge which is the lack of sufficient security within systems. Effective security has to be built-in, not patched upon. To efficaciously tackle the challenge in distributed IoT systems, we present a security context framework which applies adaptive security contexts to properly track data of interest. The proposed solution can achieve accountability and track information propagation, involving devices, services and parties who have responsibility and potential legal liability. This could help leverage not just technical but also policy and legal aspects to enable health IoT adoption.
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VDEP: VM Dependency Discovery in Multi-tier Cloud Applications
(2015-08-19)
Sangpetch, Akkarit
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Kim, Hyong S.
The automatic discovery of dependencies in distributed Cloud applications is very useful for large scale deployments. Dependencies can be used to identify the anomalies due to errors, failures or the performance bottleneck in applications. Although existing dependency models can be useful, we believe more comprehensive dependency model would improve anomaly detection in large scale distributed applications. We propose a VM dependency discovery system and introduce dependency primitives that incorporate complex application behavior/interaction patterns. We also formulate response time characteristics for each dependency primitive. Using the component dependencies and traffic monitoring, we develop a stochastic model to estimate the response time probability distribution for components and overall application. We evaluate and validate our system with various production applications. Experiments show that we can accurately discover application dependencies and also predict not only the average response time but the 95th percentile response time within 8% of the actual response time.

Sangpetch, Akkarit

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