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PigSense: Structural Vibration-based Activity and Health Monitoring System for Pigs
(2023-10-18)
Dong, Yiwen
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Bonde, Amelie
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Codling, Jesse R.
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Bannis, Adeola
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Cao, Jinpu
Precision Swine Farming has the potential to directly benefit swine health and industry profit by automatically monitoring the growth and health of pigs. We introduce the first system to use structural vibration to track animals and the first system for automated characterization of piglet group activities, including nursing, sleeping, and active times. PigSense uses physical knowledge of the structural vibration characteristics caused by pig-activity-induced load changes to recognize different behaviors of the sow and piglets. For our system to survive the harsh environment of the farrowing pen for three months, we designed simple, durable sensors for physical fault tolerance, then installed many of them, pooling their data to achieve algorithmic fault tolerance even when some do stop working. The key focus of this work was to create a robust system that can withstand challenging environments, has limited installation and maintenance requirements, and uses domain knowledge to precisely detect a variety of swine activities in noisy conditions while remaining flexible enough to adapt to future activities and applications. We provided an extensive analysis and evaluation of all-round swine activities and scenarios from our one-year field deployment across two pig farms in Thailand and the USA. To help assess the risk of crushing, farrowing sicknesses, and poor maternal behaviors, PigSense achieves an average of 97.8% and 94% for sow posture and motion monitoring, respectively, and an average of 96% and 71% for ingestion and excretion detection. To help farmers monitor piglet feeding, starvation, and illness, PigSense achieves an average of 87.7%, 89.4%, and 81.9% in predicting different levels of nursing, sleeping, and being active, respectively. In addition, we show that our monitoring of signal energy changes allows the prediction of farrowing in advance, as well as status tracking during the farrowing process and on the occasion of farrowing issues. Furthermore, PigSense also predicts the daily pattern and weight gain in the lactation cycle with 89% accuracy, a metric that can be used to monitor the piglets’ growth progress over the lactation cycle.
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GPU Performance Tuning and Power Efficiency on the DGX A100 Cluster
(2022-01-01)
Udomchoksakul, Khanin
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Sangpetch, Orathai
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Sangpetch, Akkarit
The complexity of current Deep learning has been growing rapidly nowadays. Such advancement allows various organizations such as private sectors and government to leverage intelligent systems on their use cases. High Performance Computing (HPC) infrastructure nowadays has pivoted to GPU-oriented systems, enabling developers and researchers to train complex models with large datasets unlike conventional clusters equipped only with CPU cores. However, focus on power efficiency on the HPC system has not been prevalent especially on the new system such as DGX A100 that does not have datapoints on how GPUs consumed power. Even though such HPC cluster can be powerful, always allowing it to run at the maximum capacity results to financial cost to the HPC provider at the end. Therefore, for any organization providing the system, it is crucial for them to balance the cluster capabilities while maintaining overall power consumption which can potentially be costly in the long term. This paper reveals A100 GPU metrics that are relevant to Power usage and explains GPU profiling applied to Deep learning workload on the cluster, saving up to 32% of the power usage while compromising only 11.5% of training time compared to a default profile. Then, the paper investigates literature review that could be learned further adopted to the current system at CMKL university as the next milestone.
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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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PigNet: Failure-tolerant pig activity monitoring system using structural vibration
(2021-05-18)
Bonde, Amelie
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Codling, Jesse R.
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Naruethep, Kanittha
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Dong, Yiwen
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Siripaktanakon, Wachirawich
Automated monitoring of livestock behavior can help farmers economically by detecting changes in animal welfare. Prior approaches use video, which requires light and high storage capability, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to destruction by the animals. To the best of our knowledge, we present the first system that uses structural vibration to track animal behavior, and the first system to automatically detect piglet nursing. PigNet uses vibration sensors attached to a pig pen to sense the unique vibration patterns and changes in structural response caused by the animals' movement and position within the pen. Combined with our knowledge of pig behavior, we use this physical knowledge of vibration characteristics to detect pig activities and track piglet growth in a real farm environment. Our system is designed to be robust to the harsh environment, which can create unpredictable noise, as well as physically damage or disconnect sensor nodes. When deployed in a real-world farm environment, our system was able to achieve a daily pen-level status profile of up to 90% accuracy, which tracks nursing activity, sow lying activity, and changes in piglet growth over the weeks-long pre-weaning period.
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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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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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Dependable Sensing System for Pig Farming
(2019-11-01)
Ariyadech, Sripong
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Bonde, Amelie
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Sangpetch, Orathai
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Woramontri, Woranun
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Siripaktanakon, Wachirawich
We have deployed our smart sensing system in a real commercial farm complex for at least 17 months. One of critical factors to success of the sensing system deployment is the resilience and fault tolerance of the system in a harsh environment with unreliable infrastructure and limited access. Power interruptions and intermittent connectivity is not uncommon. Sensors must work even submerging in animal excretion. Correct and continuous streams of sensor data is essential to our smart farming analytics. To make our sensing system sustain such challenges, we have designed and implemented the system with a capability of self-rejuvenation to ensure system liveness. We also equip it with our anomaly detection system to examine small sensor connectivity logs in order to identify potential faulty or deteriorating sensors or external event abnormality with minimal manual intervention.
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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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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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