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Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

Although the industrial application of drones is increasing quickly, there is a scarcity of applications in manufacturing. The purpose of this paper is to explore current and potential applications of drones in manufacturing, examine the opportunities and challenges involved and propose a research agenda.

The paper reports the result of an extensive qualitative investigation into an emerging phenomenon. The authors build on the literature on advanced manufacturing technologies. Data collected through in-depth interviews with 66 drone experts from 56 drone vendors and related services are analyzed using an inductive research design.

Drones represent a promising AMT that is expected to be used in several applications in manufacturing in the next few years. This paper proposes a typology of drone applications in manufacturing, explains opportunities and challenges involved and develops a research agenda. The typology categorizes four types of applications based on the drones’ capabilities to “see,” “sense,” “move” and “transform.”

The proposed research agenda offers a guide for future research on drones in manufacturing. There are many research opportunities in the domains of industrial engineering, technology development and behavioral operations.

Guidance on current and promising potentials of drones in manufacturing is provided to practitioners. Particularly interesting applications are those that help manufacturers “see” and “sense” data in their factories. Applications that “move” or “transform” objects are scarcer, and they make sense only in special cases in very large manufacturing facilities.

The application of drones in manufacturing is in its infancy, but is foreseen to grow rapidly over the next decade. This paper presents the first academically rigorous analysis of potential applications of drones in manufacturing. An original and theory-informed typology for drone applications is a timely contribution to the nascent literature. The research agenda presented assists the establishment of a new stream of literature on drones in manufacturing.

This study aims to reflect on the past and prospects of digital Korean studies.

Discussion includes the remarkably early adoption of computing in the Korean humanities, the astounding pace in which Korean heritage materials have been digitized, and the challenges of balancing artisanal and laboratory approaches to digital research.

The main takeaway is to reconsider the widespread tendency in the digital humanities to privilege frequentist analysis and macro-level perspectives.

Cha hopes to discover the future of digital Korean studies in semantic networks, graph databases and anthropological inquiries.

Cha reconsiders existing tendencies in the digital humanities and looks to the future of digital Korean studies.

The purpose of this paper is to introduce a three-echelon multimodal transportation problem applied to a humanitarian logistic case study that occurred in Mexico.

This study develops a methodology combining a transshipment problem and an adaptation of the multidepot heterogeneous fleet vehicle routing problem to construct a mathematical model that incorporates the use of land-based vehicles and drones. The model was applied to the case study of the Earthquake on September 19, 2017, in Mexico, using the Gurobi optimization solver.

The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time; on the flip side, the model performed in a shorter CPU time with medium and small instances than with large instances.

While the size of the instances limits the use of the model for big-scale problems, this approach manages to provide a good representation of a transportation network during a natural disaster using drones in the last-mile deliveries.

The present study contributes to a model that combines a vehicle routing problem with transshipment, multiple depots and a heterogeneous fleet including land-based vehicles and drones. There are multiple models present in the literature for these types of problems that incorporate the use of these transportation modes; however, to the best of the authors’ knowledge, there are still no proposals similar to this study.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

Climate data, including historical climate observations and climate model outputs, are often used in climate impact assessments, to explore potential climate futures. However, characteristics often associated with “islandness”, such as smallness, land boundedness and isolation, may mean that climate impact assessment methods applied at broader scales cannot simply be downscaled to island settings. This paper aims to discuss information needs and the limitations of climate models and datasets in the context of small islands and explores how such challenges might be addressed.

Reviewing existing literature, this paper explores challenges of islandness in top-down, model-led climate impact assessment and bottom-up, vulnerability-led approaches. It examines how alternative forms of knowledge production can play a role in validating models and in guiding adaptation actions at the local level and highlights decision-making techniques that can support adaptation even when data is uncertain.

Small island topography is often too detailed for global or even regional climate models to resolve, but equally, local meteorological station data may be absent or uncertain, particularly in island peripheries. However, rather than viewing the issue as decision-making with big data at the regional/global scale versus with little or no data at the small island scale, a more productive discourse can emerge by conceptualising strategies of decision-making with unconventional types of data.

This paper provides a critical overview and synthesis of issues relating to climate models, data sets and impact assessment methods as they pertain to islands, which can benefit decision makers and other end-users of climate data in island communities.