Volume 8, Issue 5

Early warning systems have had a significant impact on society by providing timely information to mitigate the effects of natural disasters. To enhance early warning capabilities, researchers are exploring the use of digital terrestrial television systems to broadcast alerts across large urban and rural areas. In this research project, the aim is to integrate the global early warning protocol CAP (Common Alerting Protocol) into the existing ISDB-T standard, alongside the standard’s Emergency Warning Broadcasting System (EWBS). This integration will enable the creation of a hybrid system, allowing various global emergency alert devices that utilize the CAP protocol to be activated through the Digital Television signal. To achieve this, a CAP to EWBS translator was developed as part of the design proposal prior to transmission. Additionally, transmitters compliant with both the full-seg and one-seg ISDB-T standards were designed to support the CAP protocol. These transmitters utilize SDR (Software-Defined Radio) cards of the Adalm Pluto type. The CAP protocol, encoded in XML format, was transmitted through the ISDB-T transport stream using the DSM-CC data transmission protocol. By incorporating the CAP protocol into the ISDB-T standard and utilizing the DSM-CC data transmission protocol, this research project aims to enhance the early warning capabilities of digital terrestrial television systems.

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The human resource development of robotics and automation in the smart factory is an important factor in “Thailand 4.0” roadmap, which is following the industry 4.0 model. To pursue this goal of Thailand 4.0 roadmap of labor development, the effective and intuitive training system must be easy to understand. This study proposes the implementation of augmented reality (AR) technology for training purposes due to its ability to visualize real-time invisible data, such as device status. This involves the development of the AR-based visual programming interface with an educational demonstration site (demo site). The AR-based training system is started with animation content explaining the smart factory concept, followed by hands-on learning using Microsoft HoloLens 2 and IoT hardware devices in demo site. The demo site using an MQTT protocol, simulates an automated packing line in the smart factory. The hardware status is published in real-time to the MQTT broker. This approach enables users to comprehend the interconnected relationship between data and hardware functionality and allowing them to create their own programs to control the IoT hardware in the demo site. With this training, 22 targeted users have successfully grasped the smart factory concept and its correlation with hardware functionality. The block-based visual programming employed in the system enables easy comprehension of robot commands. Moreover, the AR application provides a smooth display at 48-60 frames per second. The proposed system’s usability and value for specific tasks received high scores, ranging from 4 to 5 points, confirming its effectiveness for the targeted users. The proposed AR-based training system has proved that it has benefit for human resource development in the robotics and automation sector following the Thailand 4.0 roadmap. The proposed system empowered users to understand the smart factory concept and its practical implementation, leading to create new ideas for integrating AR and smart factory concepts into their manufacturing in the future.

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Unmanned aerial vehicles (UAVs) are becoming increasingly popular for both civil and military applications. Unmanned aerial vehicles can be categorized into two categories: rotary-wing and fixed-wing. Due to its capacity to fly long distances and carry substantial payloads, fixed-wing UAVs are gaining popularity and are currently utilized for various tasks. However, when confronted with disturbances such as weather or wind gusts, fixed-wing UAVs can rapidly lose stability, leading to a loss of lift and stalling. Consequently, it is vital to ensure the stability of fixed-wing UAVs. For the longitudinal stability management of a fixed wing unmanned aerial vehicle, the design and modeling of a feedback controller, including a PI controller, PID controller, and Fuzzy logic controller, are discussed in this article. MATLAB/SIMULINK©2021 will be used to design the control system and compare the response of each controller during the simulation. The controller’s response to various input formats will indicate its capacity to regulate the system’s behavior. Our results indicate that the fuzzy logic controller was superior to the PI and PID controllers at controlling the system’s response according to the desired or input behavior.

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The increase electricity production based on renewable energies sources responds to the requirements of sustainable development. In recent years, the investment in the wind energy sector has increased to satisfy green electricity generation and environment protection. Wind energy is currently the most competitive energy resource that is part of the world’s countries views on the future of energy production and exploitation and its impact on the environment, it plays a very important role to reduce the CO2 emissions injected by the energy sectors, namely transport and industry. Wind energy is a good alternative to fossil fuels as its ecological impact is low. This technology is developing rapidly of which the wind turbine industry is the most dynamic of the industries producing large equipment for power generation. Therefore, wind energy has a major drawback, which is the inability to predict accurately because of its uncontrolled fluctuations, which enter as a major component of energy flow problems within the electrical system. This paper presents the effects of wind power integration on the power quality of distribution network when the weak nature of the network in remote areas and the uncertainty of the wind are taken into consideration. The proposed model design has been tested in simulation using MATLAB/SIMULINK, where the results obtained confirm the electrical degradation in terms of quality in electrical networks integrating wind turbines, to ensure a clean electrical quality, the optimal solution is the integration of FACTS systems which will be the subject of the next paper.

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Condition assessment of medium voltage assets is essential to ensure reliability and cost-effective operation of power distribution networks. This article presents a literature review of condition assessment of medium voltage assets related to a distribution system in a non-interconnected zone in Colombia, namely, power transformers, photovoltaic systems, switchgear, lines and cables, and instrument transformers. Advanced search rules are formulated to obtain bibliographic records of relevant academic literature from the database Scopus. The retrieved data are analyzed quantitatively to provide insights on the current state of research on the topic. Next, the most relevant academic papers for each medium voltage asset are selected and analyzed in a critical review along with more diverse literature including standards, technical reports, and white papers obtained through complementary searches. The results of the review show that several approaches have been formulated for condition assessment of medium voltage assets, ranging from traditional diagnostic methods to advanced artificial intelligence-based approaches. Moreover, research on some assets is already mature including power transformers, and photovoltaic systems, whereas other assets have been incipiently studied such as distribution and instrument transformers. Therefore, the need for deeper condition assessment research for these critical assets is highlighted. Research gaps are identified in the standardization and integration of condition assessment tools for distribution system operators.

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In a challenging and increasingly technological world, it is important to promote critical thinking, multidisciplinary problem solving, and collaboration through STEAM education; however, there are important economic, administrative, and especially pedagogical manage- ment limitations for its implementation at the secondary level. Therefore, this paper presents systematic recommendations for an effective and sustainable implementation of STEAM educa- tion in educational institutions through the Gradual Multidisciplinary Model (GMM), which allows the identification and specific adaptation of STEAM knowledge through the topic of logic gates related to the representations of disjunction and conjunction in Boolean algebra (university content) to its physical representation in Minecraft (high school content). The quasi-experimental method allows to evaluate the results through the application of a pre-test designed to measure logical-mathematical thinking and a post-test designed to measure the level of understanding of practical skills and the students’ perception of the learning experience. The results obtained by t-student show that there is a significantly high difference between the means and suggest that the educational intervention orchestrated by the GMM had a significant impact on the performance and skills of the participants, since different levels of understanding (gradualness) and perception of the concepts related to logic gates could be identified; while the qualitative assessment shows the group’s willingness and enthusiasm to work with a practical and meaningful activity using Minecraft, which allows them to specifically apply their skills in science, technology, engineering, art and mathematics.

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Designing efficient control strategies for opinion dynamics is a challenging task. Understanding how individuals change their opinions in social networks is essential to countering malicious actors and fake news and mitigating their effect on the network. In many applications such as marketing design, product launches, etc., corporations often post curated news or feeds on social media to steer the users’ opinions in a desired way. We call such scenarios opinion shaping or opinion control whereby a few selected users, called control users, post opinionated messages to drive the others’ opinions to reach a given state. In this paper, we are interested in the control of opinion dynamics in social media using a combination of multi-agent systems and Q-learning. The social media environment is modeled with flexible multi-agent opinion dynamics that can capture the interaction between individuals in social media networks using a two-state updating mechanism. The environment is formulated as a partially observable multi-agent Markov decision process. We propose using intelligent reinforcement learning (RL) agents to control and shape the social network’s opinions. We present the social media network as an environment with different kinds of individuals and connections and the influencing agent as an RL agent to influence the network.

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In this paper, we delve into the pressing necessity for proficient battery cell balancing, an imperative in the context of the escalating adoption of renewable energy and electric vehicles. While traditional methodologies, including the passive technique, offer a straightforward and cost-effective solution, they compromise on efficiency. The active technique, though superior in efficiency, is hindered by its innate restriction of transferring energy solely to proximate cells, thus prolonging the balancing process. To address these limitations, we introduce a novel near-field coupling method centered on a meticulously designed resonant coil with an emphasis on achieving a larger Q-factor, a pivotal factor for enhanced battery cell balancing. This augmented Q-factor not only propels our approach past the passive method in efficiency but also catalyzes rapid balancing by enabling wireless energy transfer to cells regardless of their relative positioning. Validating our theoretical insights, we developed physical coil prototypes and adopted a Series-Parallel circuit configuration, steered by the resonant coil’s Q-factor. Preliminary experiments with three batteries substantiate our claim, showcasing that our proposed technique achieves cell balancing with approximately double the speed of conventional strategies.

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Many designs for robot arms exist. Here we present an affordable revolute arm, capable of executing simple pick-and-place tasks. The arm employs a double parallelogram structure, which ensures its endpoint angle in the plane of the upper arm remains fixed without the need for additional actuation. Its limbs are fabricated from circular tubes made from bonded carbon fiber, to ensure low moving mass while maintaining high rigidity. All custom structural elements of the arm are produced via 3D printing. We employ worm-drive DC motor actuation to ensure that stationary configurations are maintained without the necessity of continuous motor power. Our discussion encompasses an analysis of the arm’s kinematics. A simulation of the arm’s operation was carried out in MATLAB, revealing key operational metrics. In conclusion, we achieved extrinsic endpoint position tracking by implementing its inverse kinematics and PID control using a microcontroller. We also demonstrate the arm’s functionality through simple movement tracking and object manipulation tasks.

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