In order to effectively manage agriculture and promote sustainable land use in Africa, accurate mapping of irrigated croplands is imperative. This paper pioneers a novel Hybrid Vegetation Index (HVI) and integrated an approach harnessing the synergies between Radar imagery from Sentinel-1 and optical data from Sentinel-2, key vegetation indices (HVI, MSAVI), and Principal Component Analysis (PCA) to significantly enhance mapping accuracy across diverse African landscapes. Employing Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers, the combined potential of these satellite datasets is thoroughly Assessed. The integrated utilization of both Sentinel-1 and Sentinel-2 demonstrates a substantial enhancement in overall accuracy, elevating classification results by approximately 5-6% when compared to individual sensor applications. Specifically, in Kenya, Egypt, and Nigeria, the amalgamation of Sentinel-1 and Sentinel-2 enables CNN to achieve remarkable accuracies of 97.5%, 98.01%, and 98%, respectively. These findings underscore the superior performance of the fused Sentinel data and highlighted the pivotal role of their integration in advancing precise agricultural mapping. This research emphasizes the promising impact of HVI and Sentinel satellite missions in empowering informed decision-making and promoting sustainable land management practices across vital agricultural regions in Africa.
The actual paper presents an in-depth study and experimental development of a class of rotorcraft, named as x-tilt, that features four tilting rotors. Initially, the equations of motion modeling the aerial robot are presented based on the Euler-Lagrange formulation. The model includes the aerodynamic effects induced by the rotorcraft's relative motion and propellers. For control purposes the aforementioned model is split into a nominal model and lumped disturbance terms, the latter encompassing endogenous and exogenous uncertainties. In this vein, the actual work propose a robust navigation strategy targeting a specific performance profile whose problem is formulated through the model predictive control (MPC) framework. To this end, two schemes are proposed, (i) an integral MPC and a (ii) MP sliding-mode Control (MPSMC). Both control schemes are linked to a extended-state Linear Kalman Filter (ES-LKF) that furnishes the states and lumped disturbance estimates. Moreover, a high-fidelity simulation is presented in detail to validate the effectiveness of the proposed controller within a realistic scenario. We finally present the experimental stage to validate the tilting-rotor configuration as well as the integral MPC.
This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.
Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started reverberating around various disciplines. To this end, this work was undertaken: (i) firstly, to track the unfolding landscape of general issues surrounding GenAI tools; (ii) secondly, as an exploratory inquiry to elucidate the specific opportunities and limitations of GenAI tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices. As part of the investigation, we conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Further, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E, and 3DGPT) for mechanical engineering-related tasks. The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).
This study introduces a hierarchical key assignment scheme (HKAS) based on the closest vector problem in an inner product space (CVP-IPS). The proposed scheme offers a comprehensive solution with scalability, flexibility, cost-effectiveness, and high performance. Key features include CVP-IPS based construction, using two public keys for the entire scheme, a distinct basis set for each class, a direct access scheme for user convenience, and rigorous mathematical and algorithmic presentation of dynamic update operations. This scheme eliminates the need for top-down structures and offers a significant benefit in that the lengths of the basis sets defined for classes are the same and the costs associated with key derivation are the same for all classes, unlike top-down approaches, where the higher class in the hierarchy incurs much higher costs. The scheme excels in both vertical and horizontal scalability due to its utilization of the access graph and is formally proven to achieve strong key indistinguishability security (S-KI-security). This research represents a significant advancement in HKAS systems, providing tangible benefits and improved security for a wide range of use cases.
We discuss the crucial importance of explainability and understandability in artificial intelligence, in addition offering a small, insightful experiment, followed by a discussion of responses, challenges, and obstacles. We believe the pursuit of AI explainability and understandability is crucial, to be ignored at our peril.
The standard solution to new technology is to center the ethics of robotics and artificial intelligence on ”concerns” of various kinds. Many of these fears end up being rather outdated; a few are essentially accurate but barely relevant (computer technological advances will annihilate businesses that make pictures on film, audio cassettes, or vinyl records); others are essentially accurate but extremely pertinent (automobiles will cause the deaths of children and drastically alter the landscape). Some of these fears are consistently incorrect when they indicate that technology will totally transform humans. This paper analyzes the problems and deflates the non-problems.
Natural language-based interfaces are frequently used in user contact with virtual assistants to overcome accessibility issues that some user groups may encounter. Although there are worries about how AI may affect jobs and possible biases in the use of virtual assistants, the technology has promise in a number of areas, such as everyday work support and mental health therapy. This paper evaluates the uses of AI in daily life in a comprehensive and informative manner.
Due to the increasing demand for lithium-ion battery cells, the cell production processes face substantial challenges to increase productivity. Among these production processes, the assembly of electrode-separator-compounds is very relevant regarding the value added towards the process chain. However, it also represents a productivity bottleneck due to the time-consuming nature of conventional stacking processes. A novel assembly process with a rotational handling unit and continuous material flow has a significant potential to decrease the influence of this bottleneck process and to enhance the overall productivity of the process chain. However, the alignment of the electrodes within the compound is challenging. This work systematically identifies alignment principles for high-speed assembly processes in general, and for the novel assembly process in particular. By transferring the selected principles to the rotational process, suitable alignment mechanisms are developed for the assembly system. Due to their modular design, the mechanisms can be adapted to the positioning requirements of the relevant process phase and the electrode type. Consequently, the positioning mechanisms are suitable for both pre-/coarse-positioning and fine positioning and can be applied for anode, cathode, as well as laminated intermediate products. An experimental validation describes the effectiveness of the developed mechanisms for the alignment within the electrode-separator-compound for different types of electrodes. Overall, the introduction of alignment mechanisms in the assembly system leads to enhanced deposition accuracy and contributes to establishing the novel stacking process in an industrial context.
In the context of India, a country with a rich tapestry of regional sign languages, effectively recognizing and interpreting Indian Sign Language (ISL) presents a formidable challenge for individuals with hearing and speaking impairments. This system introduces an innovative method for ISL recognition by leveraging the YOLOv5s (You Only Look Once version 5) object detection framework. Complementing the YOLOv5s, the project integrates Microsoft Azure’s cognitive app service, specifically the computer vision capabilities, and utilizes Mesa, a Python agent development framework. This comprehensive approach aims to enhance the expression and communication of individuals with hearing and speaking impairments in a predominantly spoken language-oriented world.
In industrial applications, Proportional-Integral (PI) controllers are frequently employed for controlling Permanent Magnet Synchronous Motors (PMSMs) due to their fast response rate and comprehensibility. However, their control performance may deteriorate with unforeseen environmental disturbances and uncertainties. To enhance the adaptivity of the controller, Gaussian Process Regression (GPR), a machine learning technique, is used to mitigate the impact of unknown components in system dynamics in this paper. In particular, GPR is adopted to autonomously tune the parameters of the PI controller, composing a novel GPR-based PI (GPR-PI) controller that maintains both interpretability and safety, because of its theoretical prediction error bound. Moreover, the stability of the system is guaranteed under the sufficiently small designed learning rate of the PI coefficients in the gradient descent rule, indicating the tradeoff between stability and adaptivity. Then, the GPR-based PI is compared with the NN-based PI and demonstrates the priority of enlarging the stabilized speed range. Ultimately, the experiments validate the efficacy of the GPR-PI approach, showcasing a significant reduction of over 60% in response time when compared to the standard PI controller.
Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields. CNNs are not limited to Natural Language Processing (NLP) or Computer Vision (CV) but also have substantial applications in other technological domains, particularly in cybersecurity. The reliability of CNN's models can be compromised because of their susceptibility to adversarial attacks, which can be generated effortlessly, easily applied, and transferred in real-world scenarios. In this paper, we present a novel and comprehensive method to improve the strength of attacks and assess the transferability of adversarial examples in CNNs when such strength changes, as well as whether the transferability property issue exists in computer network applications. In the context of our study, we initially examined six distinct modes of attack: the Carlini and Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack. We applied these attack techniques on two popular datasets: the CIC and UNSW datasets. The outcomes of our experiment demonstrate that an improvement in transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and other attacks. Our findings further indicate that the threats to security posed by adversarial examples, even in computer network applications, necessitate the development of novel defense mechanisms to enhance the security of DL-based techniques.
In safety-and precision-critical control for permanent magnet synchronous motors (PMSMs), the spontaneous disturbance causes unexpected speed drop. The disturbance occurs without routine, so it cannot be modeled specifically. The large speed drop and slow response speed cause a reduced life of the machines driven by PMSMs. Therefore, it is crucial to implement a method that can learn the effect caused by disturbances. To this end, this paper proposes a novel approach based on the basic structure of a backpropagation neural network (BPNN) for adaptive real-time adjustment in motor control. Regarding the lack of explainability of BPNN, the electric motor physics is embedded into BPNN (BP-PHY) gradient update part to enlarge the range of stability. To overcome the shortage of a potential unstable output of neural network (NN), the learning parameter of NN is tailored based on stability theory and motor physics. Finally, the proposed methods are implemented into simulations and experiments. The recovery speed after disturbance increased to more than three times compared to the basic controller of PMSM, while the control stability of the NN is ensured.
This paper presents a comprehensive research on the application of the Design of Experiments approach to explore engineered complex systems to reveal potential detrimental weak emergent behavior. The proposed methodology utilizes orthogonal arrays in combination with regression analysis to systematically explore the parameter space of a specific system function. By screening and investigating system boundaries and potential pain points, this research introduces a novel use of orthogonal arrays to effectively detect and map areas within a system's parameter space that does not comply with defined functional acceptance criteria. The findings demonstrate that this approach enables a systematic exploration of engineered complex systems to reveal inherent detrimental weak emergent behavior, thereby enhancing test coverage, expanding system knowledge, and facilitating mitigation efforts.
Environmental movements of the late 20th century resulted in sweeping legislation and regulatory actions to reduce the prevalence of diverse pollutants. Although the consequences of noise pollution to public health, the environment, and the economy have been recognized over the same time period, noise has received far less policy attention. Correspondingly, even while recent decades have seen robust advancements in assessing the impacts of noise pollution, solutions and actual reductions in environmental noise have remained seemingly out of reach. To address this shortcoming, we developed a prospectus for environmental noise reduction through technology-forcing policies. Technology-forcing describes intent to encourage technological solutions for pollution control through policy and regulations, and has been a critical component of national and global progress in reducing environmental pollutants. We take advantage of the unique policy history for noise in the United States - which initially enacted, but then abandoned federal noise regulation. We compare this history against outcomes from contemporaneous environmental legislation for air, water, and occupational pollution control, to demonstrate the potential for technology-forcing to reduce noise pollution. Our review then identifies promising solutions, in the form of existing technologies suitable for innovation and diffusion through technology-forcing regulations and incentives. Based on this review, we outline a program for noise policy development that is intended to support efforts to reduce environmental noise pollution worldwide. The proposed program consists of three steps, 2 which are to (i) identify dominant sources of noise pollution, (ii) combine legislative or regulatory provisions with suitable systems of enforcement and incentives, and (iii) anticipate and prepare for stages of technological change. This work is intended to support and advocate for noise policies designed around technology-forcing, to advance technologies that not only improve public health and sustainable development, but ensure that these benefits are distributed equitably.
In safety-critical control for permanent magnet synchronous motors (PMSMs), overshooting after adding a spontaneous load is a crucial metric, leading to the unexpected motion of driving equipment, which induces potential unsafe problems. Therefore, it is necessary to develop a control method that effectively reduces overshoot in PMSMs. Recognizing the nature of overshoot effects, a data-driven approach, Gaussian process regression (GPR), is employed to generate the prediction. With a focus on maintaining the advantage of the GPR method, while preserving the physical properties of PMSM, an overshoot reduction-inspired motor physics embedded Gaussian Process Regression method (OR-MPE-GPR) is proposed. Inspired by the shape of the overshoot, the squared exponential (SQE) kernel function is chosen for GPR. Furthermore, by using sufficient conditions to achieve stability, the dynamic stable range and static stable range of updating rate are derived to guarantee the stability of the proposed machine learning control algorithm. Finally, comprehensive simulations and experiments compared with the state-of-the-art methods are conducted, showcasing the superior performance of the proposed method in reducing overshoot while preserving static performance within a stable region.