Real-time online optimisation plays a crucial role in high-frequency trading (HFT) strategies. The Markowitz model, as a Nobel Prize-winning framework, is widely used for portfolio management optimisation by framing the problem as a constrained quadratic programming task. While conventional analytical methods are typically effective for solving quadratic programming problems with linear constraints, the introduction of both linear equality and inequality constraints in the Markowitz model necessitates the use of numerical methods. The complexity of these numerical solutions presents technical challenges for real-time online optimisation, especially in HFT environments where computational speed and efficiency are critical. To address this challenge, we propose a simplified model that decomposes the problem into analytically solvable and unsolvable components, alongside an innovative dynamic neural network designed to quickly solve the unsolvable components. Overall, this method helps reduce computational load and is well-suited for real-time online computations in HFT settings. Furthermore, we conducted a theoretical analysis and proof of the optimality and global convergence of the solutions obtained using this method. Finally, based on a large set of real stock data, we performed three numerical experiments to validate its effectiveness. Notably, in an experiment using Dow Jones Industrial Average (DJIA) stock data, our approach reduced total costs by 5.54% compared to the commonly used MATLAB quadprog() solver, demonstrating the potential of this method as an efficient tool for portfolio management in HFT scenarios.

Disassembly is a critical step in the remanufacturing of end-of-life products. High labor costs and the limited ability of robots to perform intricate disassembly tasks have led to the increasing use of human‒robot collaboration (HRC) for disassembly. This paper addresses a challenge in HRC-based disassembly, i.e., the inherent human uncertainty during disassembly. The uncertainty is that the disassembly time for a task and the task sequence selection by a human during execution might differ from the pre-defined disassembly plan so that dynamic disassembly planning for subsequent tasks is necessary. Stackelberg equilibrium-enabled disassembly task assignment policies are designed to meet the above purpose efficiently and safely. The human leader's policy is to choose tasks that maximize the efficiency-related return value based on the robot's optimal response to the human's choice. As the follower, the robot selects tasks that maximize the safety-related return value for each human task choice. To identify the optimal values of the policies to ensure the safety and efficiency of the entire HRC-based disassembly process, an improved multi-agent proximal policy optimization (i-MAPPO) algorithm is designed. Finally, a case study for disassembling an electric vehicle battery is used to verify that the proposed approach can adapt to human uncertainty with a high success rate while ensuring that the disassembly time remains short and the human-robot distance remains within the safety threshold throughout the disassembly process.

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

This article proposes a platform for robots to learn disassembly tasks based on reinforcement learning (RL) techniques. The platform is demonstrated by a robot learning the skill of removing a bolt along a door-chain groove in a data-driven way, where the clearance between the bolt and the groove is less than 1 mm. Furthermore, the relationship between the performance of the learned skills and the precision of the robot is studied with a method to control the robot's precision by adding uncorrelated zero-mean Gaussian noise to the robot's actions. Finally, the transferability of the learned skills among robots with different precisions is empirically studied. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision.

Disassembly is a core procedure in remanufacturing. Disassembly is currently carried out mainly by human operators. It is important to reduce the labor content of disassembly through automation, to make remanufacturing more economically attractive. Threaded fastener removal is one of the most difficult disassembly tasks to be fully automated. This article presents a new method developed for automating the unfastening of screws. An electric nutrunner spindle with a geared offset adapter was fitted to the end of a collaborative robot. The position of a hexagonal headed screw in a fitted stage was known only approximately, and its orientation in the hole was unknown. The robot was programed to perform a spiral search motion to engage the tool onto the screw. A control strategy combining torque and position monitoring with active compliance was implemented. An existing robot cell was modified and utilized to demonstrate the concept and to assess the feasibility of the solution using a turbocharger as a disassembly case study. Note to Practitioners-Remanufacturing is known to generate substantial economic, social, and environmental benefits. Disassembly is the first operation in a remanufacturing process chain. Unfastening threaded parts (“unscrewing”) is a common disassembly task accounting for approximately 40% of all disassembly activity. Like other disassembly tasks, often, unscrewing has to be carried out manually in remanufacturing due to difficulties caused by the variable and unpredictable condition of the end-of-life (EoL) products to be remanufactured. Automating unscrewing operations should reduce the labor content of disassembly, thus lowering remanufacturing costs and promoting the adoption of remanufacturing. This article proposes the use of a collaborative robot to perform autonomous unfastening of hexagonal headed screws. Collaborative robots have built-in force sensors and can be programed to carry out operations involving not only position but also active force and compliance control. They can work safely alongside human operators, enabling the latter to focus on jobs requiring high cognitive or manipulation abilities. The article presents a novel spiral search technique developed to improve the rate of successful engagement between the robot end effector and the screw heads despite uncertainties in the location of the screws. The technique was successfully demonstrated on the dismantling of a turbocharger but can readily be applied to other EoL products with hexagonal headed screws. It can also be used with other kinds of screws (e.g., Phillips screws and slotted-head screws) simply by changing the tool and tuning the robot control parameters. A limitation of the proposed technique is that it can only deal reliably with undamaged screws. In our future research, we will consider screws that are in imperfect conditions through usage and develop appropriate solutions for their removal by robots.

For those shortcomings of current methods in fault diagnosis knowledge representation, it is necessary to use an efficient knowledge model to improve the accuracy of fault diagnosis and to realize the reusing and sharing of machinery fault knowledge. In this paper, an ontology-based fault diagnosis model is established. Focusing on fault diagnosis of rotating machinery, the domain-ontology knowledge base and structure definition of the fault diagnosis are demonstrated in detail. The protégé is used to construct the model of ontology-based fault diagnosis. Furthermore, rules are added and Jena is used to realize the knowledge reasoning. The result indicates that the model of fault diagnosis based on ontology is intuitive and efficient.

Faults in mechanical equipment could cause breakdown of time-critical production systems, which is very expensive in terms of production losses and re-commissioning costs. In cloud manufacturing, the scattered distribution of mechanical equipment and fault diagnosis resources, such as experts and specialist instruments, etc., could hinder the development of fault diagnosis systems. The idea of resource servitisation, aimed at resource sharing and collaboration, will lead fault diagnosis systems toward integration, low cost and high efficiency. This paper focuses on the servitisation of fault diagnosis for mechanical equipment in cloud manufacturing. A new service-oriented fault diagnosis system framework for mechanical equipment is proposed, together with a new servitisation method of fault diagnosis for mechanical equipment. Moreover, enabling technologies, e.g. XML, Web Services Definition Language (WSDL), Axis2, are also analysed. Finally, a prototype system is presented that demonstrates the feasibility and effectiveness of the developed architecture and servitisation method in a cloud manufacturing environment.

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COB's existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper.

This interdisciplinary study covers bionics, optimization and vehicle engineering. Semi-track air-cushion vehicle (STACV) provides a solution to transportation on soft terrain, whereas it also brings a new problem of excessive fuel consumption. By mimicking the foraging behaviour of honeybees, the bioinspired adaptive bees algorithm (ABA) is proposed to calculate its running parameters for fuel economy optimization. Inherited from the basic algorithm prototype, it involves parallel-operated global search and local search, which undertake exploration and exploitation, respectively. The innovation of this improved algorithm lies in the adaptive adjustment mechanism of the range of local search (called ‘patch size’) according to the source and the rate of change of the current optimum. Three gradually in-depth experiments are implemented for 143 kinds of soils. First, the two optimal STACV running parameters present the same increasing or decreasing trend with soil parameters. This result is consistent with the terramechanics-based theoretical analysis. Second, the comparisons with four alternative algorithms exhibit the ABA's effectiveness and efficiency, and accordingly highlight the advantage of the novel adaptive patch size adjustment mechanism. Third, the impacts of two selected optimizer parameters to optimization accuracy and efficiency are investigated and their recommended values are thus proposed.

Designing ergonomic interfaces for man-machine interaction is a major task for today's computer design engineers. A new type of tangible man-machine communication interface which is based on acoustics is presented in this paper. The proposed new approach is the use of pattern recognition to match the pattern of a received signal's feature with a template, acquired during a learning stage, associated with a predefined location. Theoretically this method, referred as location patten matching (LPM) can work on heterogeneous medium of any shape or material using one or two sensors. Therefore they overcome the limitations of the more widely used approaches based on time delay of arrival (TDOA).