Development of Wi-Fi-Based Teleoperation System for Forest Harvester

Kim, Gyun-Hyung; Kim, Ki-Duck; Lee, Hyeon-Seung; Choi, Yong Kee; Mun, Ho-Seong; Oh, Jung-Kwon; Shin, Beomsu

doi:10.1007/s42853-021-00100-2

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Radio Frequency Identification (RFID) tags, which communicate with readers through radio waves, are extensively used for tracking objects or individuals, particularly in inventory management and asset tracking, or, e.g., in the forestry supply chain from tree felling to the sawmill and beyond [31]. Wi-Fi positioning systems determine a device's location using the strength of nearby Wi-Fi signals and are commonly used in indoor navigation systems [32]; however, there are also some examples found outdoors, in precision agriculture [33] and forestry [34].…”

Section: Locator Devicesmentioning

confidence: 99%

Sensors for Digital Transformation in Smart Forestry

Ehrlich-Sommer,

Hoenigsberger,

Gollob

et al. 2024

Sensors

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Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

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Section: Locator Devicesmentioning

confidence: 99%

Sensors for Digital Transformation in Smart Forestry

Ehrlich-Sommer,

Hoenigsberger,

Gollob

et al. 2024

Sensors

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“…Hwang et al developed an efficient agricultural byproductcollecting device [15]. Kim et al developed a WiFi-based teleoperation system for forest harvesters to reduce the number of fatalities due to overturning accidents and harvesting costs [16]. To solve low calorific value and ununified characteristics, thermochemical conversion, especially torrefaction, should be one of the ways to overcome the drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Optimal Conditions for Oxygen-Rich and Oxygen-Lean Torrefaction of Forestry Byproduct as a Fuel

Park

Kim

et al. 2023

Energies

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Wood biomass is an alternative to fossil fuels. However, biomass use has several limitations. Torrefaction, in which reduction conditions prevail to overcome these limitations, has been suggested. Here, torrefaction using different wood chips (Liriodendron tulipifera, Populus canadensis, Pinus rigida, and Pinus koraiensis) was conducted under oxygen-rich and oxygen-lean conditions to determine the effects of oxygen. Torrefaction was conducted at 230–310 °C for 1 h. A mass yield difference of 3.53–20.02% p (percentage point) was observed between oxygen-lean and oxygen-rich conditions. The calorific value increased by a maximum of 50.95% and 48.48% under oxygen-rich and oxygen-lean conditions, respectively. Decarbonization (DC), dehydrogenation (DH), and deoxygenation (DO) occurred in the following order because of dehydration and devolatilization during biomass torrefaction: DO > DH > DC. The calorific value of the torrefied biomass increased linearly with the extent of all three processes. The combustibility index and volatile ignitability were calculated based on proximate composition to suggest the optimal conditions for replacing anthracite and bituminous coal. This study provides suggestions for stable operation in a standard boiler design.

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