TY - CHAP A1 - Ooi, Boon Yaik A1 - Lee, Wai-Kong A1 - Schubert, Martin J. W. A1 - Ooi, Yu-Wei A1 - Chin, Chee-Yang A1 - Woo, Wing-Hon T1 - A Flexible and Reliable Internet-of-Things Solution for Real-Time Production Tracking T2 - IEEE Industrial Electronics and Applications Conference (IEACon): 22.11.2021 - 23.11.2021, Penang, Malaysia N2 - The concept of Industrial Revolution 4.0 (IR4.0) has sparked the urgency of many manufacturers to revisit their manufacturing processes and search for opportunities to further improved their production output. Unfortunately, it is difficult to improve a process with inadequate data. Many of the SMEs in developing countries are still using manufacturing machines from the yesteryears which do not have computational and connectivity capabilities. Therefore, we developed an end-to-end Internet-of-Things (IoT) solution, which reliably tracks the production performance of manufacturing machines. This paper elaborates on the designs and the rationale behind it. As of the writing of this paper, our IoT system has been deployed in real manufacturing environment and has been running for approximately 90 days on a 24/7 basis without data lost. KW - industry 4.0 KW - IoT KW - production tracking KW - Smart Manufacturing Y1 - 2021 SN - 978-1-7281-9253-6 U6 - https://doi.org/10.1109/IEACon51066.2021.9654672 SP - 270 EP - 275 PB - IEEE ER - TY - JOUR A1 - Lee, Wai-Kong A1 - Schubert, Martin J. W. A1 - Ooi, Boon Yaik A1 - Ho, Stanley Jian-Qin T1 - Multi-source energy harvesting and storage for floating wireless sensor network nodes with Long Range Communication Capability JF - IEEE Transactions on Industry Applications N2 - Wireless sensor networks are widely used for environmental monitoring in remote areas. They are mainly composed of wireless sensor nodes, usually powered by batteries with limited capacity, but are expected to communicate in long range and operate for extended time periods. To overcome these limitations, many energy harvesting techniques are proposed to power wireless nodes for prolonged operation, whereas multihop techniques are utilized to extend the communication range. In this paper, a novel floating device with multisource energy harvesting technology that can be used as a wireless sensor node is proposed. The long range communication between wireless sensor nodes and a gateway is established through LoRa technology. In addition to conventional solar panels, an energy harvesting technique based on thermoelectric generators exploiting thermal differences created between water surface and materials exposed to sunlight is proposed. Energy generated from photovoltaic and thermoelectric generators is combined to power the wireless sensor node. This floating device consumes 6.6216 Wh per day when used as a wireless sensor node for the collection and transmission of environmental data. The sensor node can operate on a water surface for at least 9.6 days when it is not exposed to sunlight. During a sunny day, the floating device can harvest 8.375 Wh from solar panels and 0.425 Wh from thermoelectric generation. In other words, the floating device harvests sufficient energy to be self-sustaining during sunny days. KW - Wireless sensor networks KW - Energy harvesting KW - floating device KW - LoRa KW - sensor node KW - solar energy KW - thermoelectric generation Y1 - 2018 U6 - https://doi.org/10.1109/TIA.2018.2799 VL - 54 IS - 3 SP - 2606 EP - 2615 PB - IEEE ER - TY - CHAP A1 - Schubert, Martin J. W. A1 - Lee, Wai-Kong A1 - Ooi, Boon Yaik T1 - Quantization and Sampling Effects in Fast Digitally Controlled Switch-Mode DC/DC Buck Converters T2 - 2021 IEEE Industrial Electronics and Applications Conference (IEACon): 22.11.2021 - 23.11.2021, Penang, Malaysia N2 - Software-defined control of DC/DC switch mode power converters features unequaled flexibility compared to analog control. For fast switching converters as recently used for low power applications, digital control may cause steady-state oscillations known as limit cycles. This effect based on a quantized duty cycle is compared to other aberrations such as output ripple voltage, which is inherent to any pulse-width modulator. A distinction is made between DC and AC errors. The latter are divided into synchronous and asynchronous output voltage disturbances. Synchronous AC errors are composed of the switching frequency and its harmonics. KW - aberrations due to digitization KW - digital pulse-width modulation (DPWM) KW - low power DC/DC buck conversion KW - output voltage sampling Y1 - 2021 SN - 978-1-7281-9253-6 U6 - https://doi.org/10.1109/IEACon51066.2021.9654272 SP - 56 EP - 61 PB - IEEE ER - TY - CHAP A1 - Ho, Joe Ee A1 - Ooi, Boon Yaik A1 - Westner, Markus T1 - Application Integration Framework for Large Language Models T2 - 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand, 03-04 September 2024 N2 - Large Language Models (LLMs) have unlocked new opportunities in processing non-structured information. However, integrating LLM into conventional applications poses challenges due to their non-deterministic nature. This paper introduces a framework designed to effectively integrate LLM into intermediate modules by ensuring more consistent and reliable outputs. The framework includes three key components: the Sieve, which captures and retries processing of incorrect outputs; the Circuit Breaker, which stops processing persistently incorrect outputs; and the Optimizer, which enhances processing efficiency by combining inputs into single prompts. Experimental results employing structured methodology demonstrate the framework's effectiveness, achieving significant improvements a 71.05% reduction in processing time and an 82.97% reduction in token usage while maintaining high accuracy. The proposed framework, agnostic to specific LLM implementations, aids the integration of LLMs into diverse applications, enhancing automation and efficiency in fields such as finance, healthcare, and education. KW - error handling KW - token efficiency KW - AI integration KW - data pipeline optimization KW - automation KW - data processing framework Y1 - 2024 SN - 979-8-3315-2855-3 U6 - https://doi.org/10.1109/AiDAS63860.2024.10730541 SP - 1 EP - 6 PB - IEEE ER -