Brain–computer interfaces (BCIs) enable people to communicate with others or devices, and improving BCI performance is essential for developing real-life applications. In this study, a steady-state visual evoked potential-based BCI (SSVEP-based BCI) with multi-domain features and multi-task learning is developed. To accurately represent the characteristics of an SSVEP signal, SSVEP signals in the time and frequency domains are selected as multi-domain features. Convolutional neural networks are separately used for time and frequency domain signals to extract the embedding features effectively. An element-wise addition operation and batch normalization are applied to fuse the time- and frequency-domain features. A sequence of convolutional neural networks is then adopted to find discriminative embedding features for classification. Finally, multi-task learning-based neural networks are used to detect the corresponding stimuli correctly. The experimental results showed that the proposed approach outperforms EEGNet, multi-task learning-based neural networks, canonical correlation analysis (CCA), and filter bank CCA (FBCCA). Additionally, the proposed approach is more suitable for developing real-time BCIs than a system where an input’s duration is 4 s. In the future, utilizing multi-task learning to learn the properties of the embedding features extracted from FBCCA can further improve the BCI system performance.
Path planning enables autonomous agents such as robots, self-driving vehicles, and UAVs to navigate from a starting point to a target destination while avoiding obstacles and adhering to operational constraints. As autonomous technologies become more prevalent in real-world applications, the demand for robust, adaptive, and computationally efficient path planning algorithms has intensified. This paper presents a comprehensive review of path planning strategies, focusing on classical, metaheuristic, and AI-based approaches. It explores the challenges posed by dynamic environments, non-holonomic constraints, and varying levels of environmental knowledge. The review also examines the strengths and limitations of each algorithmic category, highlighting their suitability for diverse applications ranging from industrial automation to autonomous navigation. Furthermore, the paper discusses emerging trends, including the integration of machine learning and reinforcement learning techniques, and outlines future research directions aimed at enhancing the adaptability and performance of path planning systems in complex, unstructured environments.
Abstract To address the challenges of low efficiency, complex processes, low accuracy, and high costs in financial management, this paper proposes utilizing blockchain and IoT technologies, specifically Blockchain-based Smart Contract and Biometric Multifactor Authentication (BCSC-BMFA), to develop an intelligent financial management system for technical institutions. The proposed BMFA system aims to provide secure and transparent data management, real-time monitoring and reporting, and automation of financial processes to improve accuracy, efficiency, and transparency. Blockchain-based ledgers are used to store financial data securely, along with IoT sensors such as Point-of-Sale (POS) sensors and asset tracking sensors, to capture real-time financial data, and smart contracts to automate financial processes. This framework improves accuracy and efficiency, reduces costs, and increases transparency and accountability. The system’s efficiency is evaluated using a pilot study to demonstrate its performance and effectiveness in a real-world scenario.
Sebastian Schmidt, Monika Friedemann, David Hanny
et al.
When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers.
The optimal dispatching of renewable energy power stations is particularly crucial in scenarios where the stations face energy rationing due to the large proportion of renewable energy integrated into the power system. In order to achieve safe, economical, and fair scheduling of renewable energy power stations, this paper proposes a two-stage scheduling framework. Specifically, in the initial stage, the maximum consumption space of renewable energy for the system can be optimized by optimizing the formulated safe-economic dispatch model. In the second stage, the fair allocation mechanism of renewable energy power stations is proposed based on the game-fairness empowerment approach. In order to obtain a comprehensive evaluation of renewable energy power stations, an evaluation index system is constructed considering equipment performance, output characteristics, reliability, flexibility, and economy. Subsequently, the cooperative game weighting method is proposed to rank the performance of renewable energy power stations as the basis for fair dispatching. Simulation results show that the proposed scheduling strategy can effectively ensure the priority of renewable energy power stations based on their comprehensive ranking, and improve the safety, economy, and fairness of power station participation in scheduling.
[Significance]The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.[Progress]Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.[Conclusions and Prospects]To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.
Під час робочого процесу навантажувач перемішується на майже горизонтальних майданчиках, допустимий ухил яких. Розрахунок поздовжньої стійкості навантажувачів ведеться з умови перекидання вперед з урахуванням того, що деформуються пневматичні шини, якщо пневмоколісний хід. Кут додаткового нахилу навантажувача вперед внаслідок деформації опор визначається співвідношенням сили тяжкості навантажувача з вантажем жорсткість ґрунту під переднім та заднім котками гусеничного ходу або радіальна жорсткість передніх та задніх пневматичних шин навантажувача на пневмоколісному ході; відстань між центром ваги навантажувача та вертикальною віссю, що проходить через точку перекидання. Тому при розрахунку поздовжньої стійкості гусеничного та пневмоколісного навантажувачів. Найменший запас поздовжньої стійкості має навантажувач у разі руху під ухил з одночасним гальмуванням машини та робочого обладнання при його опусканні. Положення робочого обладнання відповідає максимальному вильоту.
The ITExpo conference was held in Fort Lauderdale, Florida in February 2023. The IoT Evolution program is part of the overall ITExpo conference and focuses on emerging trends and opportunities within the Internet of Things (IoT) environment. IoT Evolution offers expert sessions on practical applications and use cases of IoT. These include automation, security, and healthcare (https://www.iotevolutionexpo.com/east/). One of the 2023 conference sessions was entitled “Medical Internet of Things (MIoT) – Opportunities for Managed Solutions Providers (MSPs). This article is from the presenter’s perspective and addresses the topics covered in the doctoral dissertation research completed in 2022. The focus of the session was to provide information to organizations that offer managed solutions services to clients or customers, including healthcare. One of the observations derived from the doctoral research was a lack of awareness within the healthcare community regarding the security and privacy issues associated with remote implantable or wearable medical devices. Doctors presumed that these issues were addressed by the device manufacturers, HIPAA, the FDA, or others. Research indicated that this was not correct and that there was a gap in this area. This gap represented an opportunity for organizations like MSPs that provide consulting and advisory services to healthcare organizations regarding overall security and privacy. The article elaborates on the composition of the attendees, questions that arose during the session, and summarizes the information that was provided. The linkage between academic research and practical field application were key elements of this session.
History of scholarship and learning. The humanities
This paper describes an algorithm for extracting human gait movements in data obtained from accelerometer sensors of a mobile phone, provided that the mobile phone is used in the usual mode. The algorithm also performs a classification of the selected movements based on a feed-forward neural network. The developed algorithm selects the best areas in the accelerometer data, which reflect individual steps, according to the optimality criterion. For the selected area, the optimality criterion is the maximum value of the correlation coefficient with all other data segments. The selected plots are used as templates. Changing the parameters of patterns over time is necessary to assess changes in the individual rate of the functioning of the musculoskeletal system. Due to the correction of tolerance limits at the segmentation stage, the algorithm adapts to the change in gait speed.
In the integrated electrical systems, frequency control service considering the electric vehicle (EV) aggregators could lead to time-varying delay in load frequency control (LFC) schemes. These delays influence the LFC system efficiency, and in some situations, the lack of a clear choice of a control strategy considering the time-varying delays causes power system instability. Thus, this paper illustrates different time-varying delays based on the stability of an LFC system in the EV aggregators presence. The LFC's delay-dependent stability study is executed for finding the stability region and, stability criteria is suggested using the linear matrix inequality (LMI) method and Lyapunov-Krasovskii theory. Also, Wirtinger-based improved inequality and bounding lemma are applied to compute the greatest allowable delay in the LFC system, including the EV aggregators.
Electrical engineering. Electronics. Nuclear engineering, Production of electric energy or power. Powerplants. Central stations
Magdalena Tutak, Jarosław Brodny, Antoni John
et al.
Dust is one of the most burdensome hazards found in the environment. It is composed of crushed solids that pose a threat to the health and life of people, machines and machine components. At high concentration levels, it can reduce visibility. All of these negative phenomena occur during the process of underground mining, where dust hazards are common. The negative impact of dust on the efficacy of the mining process prompts research in this area. The following study presents a method developed for model studies of dust dispersion in driven dog headings. This issue is immensely important due to the fact that these dog headings belong to a group of unidirectional excavations (including tunnelling). This paper presents the results of model studies on dust dispersion in driven dog headings. The main focus is on the analysis of the distribution of dust concentration along a dog heading during the mining process. In order to achieve this goal, a model test method based on the finite volume method, which is included in the group of CFD methods, was developed. Analyses were carried out for two different values of dust emission from the face of the excavation for the transient state. The results made it possible to determine areas with the highest potential for dust concentration. The size and location of these areas are mainly dependent on the amount of dust emissions during the mining process. The results can support the process of managing dust prevention and protection of workers during the mining excavation process.
Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.