Для підтвердження застосовності теоретичного методу синтезу оптимального керування рухом пристрою для транспортування малогабаритних вантажів постає питання проведення експериментального дослідження такого керування на практиці. В даній роботі описано методику проведення експериментального дослідження процесу стабілізації положення пристрою для транспортування малогабаритних вантажів та методи оцінки якості такої стабілізації.
Очікуваним результатом було отримати експериментальні дані перевірки якості розробленого керування для 11 наборів коефіцієнтів ПІД-регулятора. Надалі з них обрано коефіцієнти регулятора, які найкраще себе показали в процесі стабілізації положення пристрою. Також отримано експериментальні дані роботи пристрою з мінімальною похибкою при порівнянні з теоретичними даними.
При проведенні експериментального дослідження використано фізичну модель двоколісного пристрою для транспортування малогабаритних вантажів. Було перевірено якість реалізації регулювання положення пристрою на одинадцяти наборах коефіцієнтів ПІД-регулятора. Зібрано масиви експериментальних даних роботи пристрою, проведено порівняння з теоретичними даними та проведено оцінку якості процесу стабілізації положення пристрою.
При співставленні теоретичних і експериментальних даних отримано показники максимальних та середньоквадратичних похибок кута нахилу пристрою, показники похибок максимальної та середньоквадратичної кутової швидкості нахилу пристрою. Декремент згасання коливань знаходився в межах 0,25…2,11. Серед усіх розв’язків обрано найкращим з практичної точки зору є результат набору наступних коефіцієнтів ПІД-регулятора: пропрорційний k1=-2,112, інтегральний k2=-1,756, диференціальний k3=-1,38·10-7. Цей результат відповідає найбільшому декременту згасання коливань (λ=2,11). Отриманий результат дав підстави вважати методику синтезу оптимального керування дієвою, а задачу експериментальної перевірки виконаною.
Amid China's dual-carbon transition, the synergistic alignment of green finance with green-technology innovation is pivotal for co-controlling pollution and CO2 emissions. Using panel data for 266 Chinese prefecture-level cities over 2007-2023, We construct the coupling coordination index system of green finance and green technology innovation via a coupling-coordination model and systematically analyzes influencing mechanism of synergistic effect of pollution and carbon reduction. Four findings emerge.(1) The coupled-coordination significantly enhances the synergy, and energy efficiency plays a partial intermediary role in the relationship between the two.(2) The effect is heterogeneous: pronounced in the eastern and western regions, negligible in the central region, and stronger in non-resource-based and non-Yangtze River Basin cities.(3) A double-threshold model reveals a non-linear strengthening pattern as green-finance depth increases.(4) Spatial Durbin estimates show positive spillovers: the coupling of green finance and green technology innovation not only improves the level of local coordination, but also drives the improvement of environmental performance in adjacent areas. These results provide quantitative guidance for allocating green-finance resources, elevating green-innovation efficiency, and designing regionally coordinated mitigation policies.
Automating design minimizes errors, accelerates the design process, and reduces cost. However, automating robot design is challenging due to recursive constraints, multiple design objectives, and cross-domain design complexity possibly spanning multiple abstraction layers. Here we look at the problem of component selection, a combinatorial optimization problem in which a designer, given a robot model, must select compatible components from an extensive catalog. The goal is to satisfy high-level task specifications while optimally balancing trade-offs between competing design objectives. In this paper, we extend our previous constraint programming approach to multi-objective design problems and propose the novel technique of monotone subsystem decomposition to efficiently compute a Pareto front of solutions for large-scale problems. We prove that subsystems can be optimized for their Pareto fronts and, under certain conditions, these results can be used to determine a globally optimal Pareto front. Furthermore, subsystems serve as an intuitive design abstraction and can be reused across various design problems. Using an example quadcopter design problem, we compare our method to a linear programming approach and demonstrate our method scales better for large catalogs, solving a multi-objective problem of 10^25 component combinations in seconds. We then expand the original problem and solve a task-oriented, multi-objective design problem to build a fleet of quadcopters to deliver packages. We compute a Pareto front of solutions in seconds where each solution contains an optimal component-level design and an optimal package delivery schedule for each quadcopter.
Cultural evolution allows ideas and technology to build over generations, a process reaching its most complex and open-ended form in humans. While social learning enables the transmission of such innovations, the cognitive processes that generate innovations remain unclear. We propose that semantic knowledge-the associations linking concepts to their properties and functions-guides human innovation and drives cumulative culture. To test this, we combined an agent-based model, which examines how semantic knowledge shapes cultural evolutionary dynamics, with a large-scale behavioural experiment (N = 1,243) testing its role in human innovation. Semantic knowledge directed exploration toward meaningful solutions and interacted synergistically with social learning to amplify innovation and cultural evolution. Participants lacking access to semantic knowledge performed no better than chance, even when social information was available, and relied on shallow exploration strategies for innovation. Together, these findings indicate that semantic knowledge is a key cognitive process enabling human cumulative culture.
Debora Princepe, Cristobal Quiñinao, Cristina Díaz Faloh
et al.
Can sustained open-ended technological progress preserve natural resources in a finite planet? We address this question on the basis of a stylized model with genuine open-ended technological innovation, where an innovation event corresponds to a random draw of a technology in the space of the parameters that define how it impacts the environment and how it interacts with the population. Technological innovation is endogenous because an innovation may invade if it satisfies constraints which depend on the state of the environment and of the population. We find that open-ended innovation leads either to a sustainable future where global population saturates and the environment is preserved, or to exploding population and a vanishing environment. What drives the transition between these two phases is not the level of environmental impact of technologies, but rather the demographic effects of technologies and labor productivity. Low demographic impact and high labor productivity (as in several western countries today) result in a Schumpeterian dynamics where new "greener" technologies displace older ones, thereby reducing the overall environmental impact. In this scenario, global population saturates to a finite value, imposing strong selective pressure on technological innovation. When technologies contribute significantly to demographic growth and/or labor productivity is low, technological innovation runs unrestrained, population grows unbounded, while the environment collapses. As such, our model captures subtle feedback effects between technological progress, demography and sustainability that rationalize and align with empirical observations of a demographic transition and the environmental Kuznets curve, without deriving it from profit maximization based on individual incentives.
Alessandro Tavazzi, Dimitri Percia David, Julian Jang-Jaccard
et al.
Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation. Unlike previous studies focusing on the binary relationship between a paper and the concept it attributes to technology, our approach utilizes attribution scores to enhance the relationships between research papers, combining keywords, citation rates, and collaboration status with specific technological concepts. The proposed method integrates text mining and bibliometric analyses to formulate and predict technological proximity indices for encryption technologies using the "OpenAlex" catalog. Our case study findings highlight a significant convergence between blockchain and public-key cryptography, evidenced by the increasing proximity indices. These results offer valuable strategic insights for those contemplating investments in these domains.
Technological innovations are revolutionizing public health by enhancing the accessibility, efficiency, and quality of health services. This paper explores various technological advancements such as big data analytics, artificial intelligence, mobile health applications, and wearable devices, which are transforming public health. Big data analytics enable faster and more efficient health data processing, aiding in early diagnosis, outbreak prevention, and informed health policy-making. Mobile health applications and telehealth services improve access to healthcare, allowing individuals to monitor their health, communicate with healthcare professionals, and receive services remotely. Wearable devices offer continuous health monitoring, enhancing disease detection and health management. These innovations collectively increase the effectiveness and efficiency of health services, contributing to better overall public health outcomes.
Francisco Paredes-Leon, Marisela Rodriguez-Salvador, Pedro F. Castillo-Valdez
This paper examines the benefits of and barriers to technology transfers from academia to industry perceived by entrepreneurs and particularly associated with the dimensions of Entrepreneurial Capacity. This study is one of the first in which the analysis of the topic goes beyond the high-tech sectors. It is based on a survey of representatives of Small and Medium Enterprises (SMEs) dedicated to the production of Leather and Footwear in Peru. The main findings were that the Absorption Capability dimension had a positive relationship coefficient with the understanding of the benefits of and barriers to technology transfers, while the Networking Diversity dimension presented a negative relationship coefficient. Likewise, this study shows that the main barriers to technology transfer were the fear of information leaks and the lack of training. The results of this research can add value to decision makers in industry, academia, and government agencies interested in science and technology policies.
<p>In a linear economy, manufacturing is less costly and more profitable than remanufacturing because of reduced private costs of utilization and production. However, manufacturing also involves higher resource extraction and waste as externalized costs than remanufacturing. We use a vintage capital framework to assess technological innovations in remanufacturing and their potential benefits to society and human occupations. Our study shows that replacing manufacturing with remanufacturing technologies creates positive static and dynamic circular economy externalities. These externalities can be quantified to assess improvements in social outcomes. A smartphone remanufacturing innovation case study is presented as an illustration of the article’s main ideas. Future research should investigate additional specific cases to develop a comprehensive methodology for assessing the impact of remanufacturing innovations on social outcomes. This will provide valuable insights into the broader implications of remanufacturing practices.</p>
Houshyar Honar Pajooh, Serge Demidenko, Saad Aslam
et al.
Ubiquitous computing turns into a reality with the emergence of the Internet of Things (IoT) adopted to connect massive numbers of smart and autonomous devices for various applications. 6G-enabled IoT technology provides a platform for information collection and processing at high speed and with low latency. However, there are still issues that need to be addressed in an extended connectivity environment, particularly the security and privacy domain challenges. In addition, the traditional centralized architecture is often unable to address problems associated with access control management, interoperability of different devices, the possible existence of a single point of failure, and extensive computational overhead. Considering the evolution of decentralized access control mechanisms, it is necessary to provide robust security and privacy in various IoT-enabled industrial applications. The emergence of blockchain technology has changed the way information is shared. Blockchain can establish trust in a secure and distributed platform while eliminating the need for third-party authorities. We believe the coalition of 6G-enabled IoT and blockchain can potentially address many problems. This paper is dedicated to discussing the advantages, challenges, and future research directions of integrating 6G-enabled IoT and blockchain technology for various applications such as smart homes, smart cities, healthcare, supply chain, vehicle automation, etc.
Engineering machinery, tools, and implements, Technological innovations. Automation
ABSTRACTIn the last decade, the European Commission (EC) developed an ambitious strategy to promote RRI across the Horizon 2020 Framework Programme for Research and Innovation (H2020). This effort resulted in a significant number of European-funded projects that substantially expanded the available knowledge of the theory, methods and implementation of RRI. However, various evaluations and studies revealed a limited and diffuse implementation of the concept. In this article, we aim to shed some light on this matter with a study covering eight programme lines of H2020 (ERC, MSCA, LEIT, FOOD, ENV, SEC, WIDENING and EURATOM). We employ an extensive policy document analysis and 112 semi-structured interviews carried out with various stakeholders. We argue that the limited implementation of RRI in H2020 is the result of conflicts with existing values, science cultures, economic objectives, restricted resources for its implementation and a lack of clarification around what RRI means.
Abstract This survey provides a review of the fundamental approaches to design for mass customisation (DFMC), design for manufacturing (DFM) and design for supply chains (DFSC). The key term here is design while mass customisation, manufacturing and supply chain are the contexts from which the respective design objectives are derived. While these three areas of design are closely related, they have different focusses, which is reflected in the broader range of approaches proposed in the literature. The authors look at the literature through the lens of the product, process, and supply chain optimisation, with a variety of objectives ranging from improving product quality and variety while reducing costs, minimising environmental impacts and optimising supplier manufacture cooperation. In addition to the reviews of the approaches to DFMC, DFM and DFSC, recommendations on their practical system implementations are provided. While the authors acknowledge that the richness of the literature of each of the three design areas warrants a dedicated literature review, the main purpose of this survey is to pursue an integrated view on the three design issues faced by modern manufacturers and provide them and other related practitioners with a summary of representative approaches in the literature. Although it was not intended to conduct an exhaustive literature review of the literature, researchers from academia may still find the work useful by looking at the interactions of the three design areas from the perspective of joint‐decision making, which is the angle from which the literature is approached.
This paper presents a methodology that forms an automated tool for robot kinematic representation conversion, called the RobKin Interpreter. It is a set of analytical algorithms that apply basic linear algebra tools that can analyze an input serial robot representation, express the joints globally in matrix form, and map to other representations such as standard Denavit-Hartenberg parameters, Roll-Pitch-Yaw angles with translational displacement, and Product of Exponentials with a possibility to generate a URDF (Universal Robot Description Format) file from any of them. It works for revolute and prismatic joints and can interpret even arbitrary kinematic structures that do not have orthogonally placed joints and often appear in flexible robotic systems. The aim of the proposed methods is to facilitate the necessary switches between representations due to software compatibility, calculation simplification, or cooperation.
Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets to a fixed initial state distribution at the end of each episode, to successfully train the agents from repeated trials. Such reset mechanism, while trivial for simulated tasks, can be challenging to provide for real-world robotics tasks. Resets in robotic systems often require extensive human supervision and task-specific workarounds, which contradicts the goal of autonomous robot learning. In this paper, we propose an extension to conventional reinforcement learning towards greater autonomy by introducing an additional agent that learns to reset in a self-supervised manner. The reset agent preemptively triggers a reset to prevent manual resets and implicitly imposes a curriculum for the forward agent. We apply our method to learn from scratch on a suite of simulated and real-world continuous control tasks and demonstrate that the reset agent successfully learns to reduce manual resets whilst also allowing the forward policy to improve gradually over time.