Researchers developed a control method that allows robots to better lift and move patients without compensating for friction, improving patient safety and comfort. The next step is to add a torso to the robot's arm, making it more human-like.
A new AI-powered method predicts building energy consumption by analyzing environmental and operational parameters, offering a significant improvement in energy management. The hybrid deep learning model has the potential to be applied in various smart buildings and cities.
Researchers propose a new computational analysis method that uses keystroke time series data to detect early stages of Parkinson's disease. The method, based on fuzzy recurrence plots, is less physically demanding than traditional testing methods and shows encouraging results.
A team of researchers developed a way for multiple UAVs to form intelligent teams while maintaining individual control, enabling more efficient and adaptable flight operations. The technique uses software-in-the-loop testing and simulation environments to optimize UAV performance in varying environmental conditions.
A new control system has been developed to better control wind energy systems, improving efficiency and reducing costs. By decoupling power generation and feedback, the system enables turbines to respond quicker and with less strain on physical components.
Researchers argue that using cooperative game theory to model human-drone interactions can lead to better steering angle control and safer lane-change maneuvers. By taking into account human drivers' real-life experience, automated steering technology can achieve better shared control.
A team of international researchers has developed a new control algorithm that reduces the complexity of bilateral teleoperation systems while maintaining their performance. The proposed composited stated convergence scheme achieves this by reducing communication channels, resulting in better transient performance.
A new algorithm assesses cloudlet placement to achieve an optimal balance between cost and service performance. The proposed method uses cloudlets, tiny versions of the cloud, to improve network service performance for a limited number of users.
A team of Australian researchers has designed a reliable strategy for testing physical abilities of humanoid robots using machine learning methods and algorithms. The findings have promising implications in the broad use of humanoid robots in fields such as healthcare, education, disaster response, and entertainment.
Researchers developed an AI method to recognize patterns in infant cries, enabling healthcare workers and parents to accurately determine a baby's needs. The algorithm uses compressed sensing to process noisy environments, allowing for independent use across various scenarios.
A new method enables efficient use of energy by smart homes in a microgrid, reducing overall load and demand for renewable energy. The approach allows smart homes to exchange surplus renewable energy, optimizing energy use and scheduling.
Researchers in USA develop first-ever standardized method to evaluate commercially available driver-monitoring systems. The method assesses drivers' distractions and provides a common base for comparing different recording devices.
A new microgrid system designed by American and Chinese researchers promises improved stability, safety, and resilience in delivering energy. The system aligns several energy sources in parallel and uses a decentralized control algorithm to overcome the burdens of system overload and shutdown.
Researchers develop method to mimic human decision-making, enabling computers to render multiple acceptable decisions. This allows for variability in computer systems, similar to human experts, and can lead to better performance.
A research team at the University of Illinois developed a Stackelberg feedback solution to counter jamming signals, enabling more reliable communication in critical systems. The approach involves coordinated actions among sensor, encoder, and decoder units to minimize errors caused by jamming.
Researchers developed a wireless sensor network capable of recording ecosystem sounds with high quality, while being energy-efficient and cost-effective. The system aims to streamline biodiversity monitoring and address the global need for new assessment methods.
Researchers from Mitsubishi Electric Research Laboratories developed an improved algorithm to track motor performance and speed estimation without sensors. The proposed algorithm uses state variables to estimate rotor speed, addressing limitations in existing approaches.
A team of researchers has developed a minimal logic system to bridge the gap between mathematical proofs, algorithms, and real-world outcomes in control systems. The work, published in IEEE/CAA Journal of Automatica Sinica, aims to improve the realism of theoretical mathematics by focusing on computational certainty.
Researchers developed a computer game to examine relationships between countries and strategic environments. They found that having more allies can increase a country's responsibility, leading to decreased overall welfare. The study has potential implications for current events, including the China-US-North Korea conflict.
Researchers from Zhejiang University developed a new way to improve automated systems, such as energy plants and airplanes, using polyhedral feasible set computation. This method efficiently analyzes current behaviors and time frames to predict the best next steps for optimal performance.
Researchers developed a coordinated control architecture for motion management in ADAS systems, demonstrating improved safety and comfort. The study showed that 'coordinated control' strategy successfully damped out deviation errors, giving much greater precision in following intended trajectories.
Researchers developed a new method to train computers to better recognize objects in the real world by using virtual reality. A virtual dataset called ParallelEye was created, allowing for diverse and realistic images of various scenes, which significantly improved performance on object detection tasks.
A new 'smart' algorithm is proposed to manage household energy usage, taking into account varying power sources like windmills and solar panels. The goal is to minimize costs while meeting energy demand, with the potential for future optimization through machine learning.
Researchers propose social manufacturing as an innovative solution for the personalized customization era, allowing consumers to participate in the design and production process. This approach can reduce material waste and energy consumption while satisfying both material and spiritual needs.
A new design proposes a top-level intelligent dispatch system that incorporates artificial systems and real-world input to find the best way to dispatch power quickly and efficiently. By leveraging historical operation actions and records, the dispatch system can convert human experience into intelligent technical models.
Researchers from Chinese Academy of Sciences developed a 7-DOF haptic interface allowing for seven degrees of movement. The interface enables true haptic interaction and can be used in medical simulation, virtual assembly and remote manipulation tasks.
The new approach enhances audio-analysis machines to process noisy environments by combining scalograms and spectrograms with convolutional neural networks. This improves sound classification, enabling better identification of isolated sounds like gunshots and music or speech in complex scenes.
Researchers create a data-driven method to detect and track human movements with high accuracy, focusing on the entire body rather than just the core. The new technique uses a time-of-flight camera to scan the subject's range of view and identify five extreme points: head, hands, and feet.
Researchers develop methods to regulate robot movements and human interactions, aiming to improve social acceptance of robots in mobile robotics. The proposed control improves the robot's ability to emulate human-human dynamic behavior, reducing collisions and improving its acceptance by humans.
A model predictive control framework incorporates adaptable interaction models for personalized human-robot collaboration, resulting in precise shared movements. Personalization allows robots to learn from users and adjust their interference to optimize performance.
Researchers developed an algorithm to improve the control of series elastic actuators in robots, enabling better handling of unknown payload parameters and external disturbances. The new method allows for more flexible and precise robotic movements, improving human-robot interaction safety.
A team of researchers from King Fahd University of Petroleum & Minerals proposed a control design for the I-PENTAR wheeled inverted pendulum assistant robot to tackle stability issues and uncertainty. The algorithm improved the robot's ability to maintain balance even in uncertain environments.
A team of researchers from Cranfield University and Chinese institutions developed a framework combining cyber, physical, and social systems to integrate vehicle connectivity and automation attributes. They proposed the use of parallel learning theory to analyze information regarding vehicle, human driver, and driving actions in parallel.
Scientists have developed a new algorithm that can mirror a microgrid's inertia by adjusting the photovoltaic system's direct current. The solution aims to make microgrids work like large power grids with high inertia, improving stability and reliability.
Researchers design a second-order sliding mode controller to eliminate frequency fluctuations and improve control of DC-DC buck converters. The new method retains robustness while allowing flexibility in the sliding line parameter.
Researchers developed a methodology to analyze the interplay between social and technical aspects of energy systems. By integrating data from various variables, they found that occupancy and energy consumption were influenced by factors such as indoor temperature and perceived air quality.
A new method for controlling self-balancing mobile robots has been proposed by Prof. Mou Chen, improving their tracking performance. The technique utilizes a disturbance observer to fully utilize dynamic information and adjust the robot's behavior.
Researchers from Case Western Reserve University proposed a new method to calculate and correct short-circuit errors in large-scale distribution systems. The method, which was published in IEEE/CAA Journal of Automatica Sinica, uses real-time simulations and modeling to optimize the system in just 74 milliseconds.
Researchers propose a day-ahead economic dispatch model to optimize wind power generation, considering both planned and real-time energy use. The framework successfully predicts optimal models for energy commitment and has been tested in real-world simulations.
Researchers developed a parametric genetic algorithm to assess complementary options for large-scale wind-solar coupling in the electrical grid. The approach successfully optimized power generation and dispatch decisions, showing promise for managing intermittent renewable energy sources.
Scientists developed an algorithm to accurately identify multiple signals at multiple levels in the circuit to detect open-switch faults. The combination of the algorithm and artificial neural network can improve microgrid reliability, efficiency, and cost.
Researchers propose pairing remote wind farms with conventional units to reduce power storage needs, improving reliability. The approach uses an algorithm to estimate future wind states and provides limitations for extreme wind states.
Researchers have developed a new algorithm to optimize battery power consumption in smart home systems. The system learns to adapt to real-time electricity rates and minimizes grid power needed while extending battery life. Future work will investigate avoiding damage caused by frequent charging and discharging modes.
A team of mathematicians from the University of Averio combined past and future operators to create a new theory for fractional calculus, which can predict how variables change and react. This breakthrough has implications for various technologies and applications, including information exchange and automation.
A new model predicts wind frequency and potential contributions to traditional energy sources, enabling decentralized load frequency control. The algorithm requires less computational time than traditional methods, but may require more computation to maintain system stability.
A team of researchers from the Polytechnic University of Bari proposes a new control scheme that improves dynamic performance and disturbance rejection. They also introduce an improved pre-filter to assist the system in maintaining attention on relevant data, enabling quicker integration and acceptance.
A novel control scheme is proposed to control unknown nonlinear systems without exact knowledge of system dynamics. The method uses an observer-based approach with a neural network to observe the system at multiple time scales and update its information.
Scientists found that models with only two variables work best in combining drugs, reducing the risk of overfitting. The algorithm can be used to optimize various types of drug combinations, including those with multiple biological molecules.
Researchers developed a new setpoint-tracking strategy using fractional calculus to improve the response time and stability of automated systems. The approach outperformed classical integer-order filters in tracking complex paths, offering potential benefits for applications like robotics, self-driving cars, and medical devices.
Researchers created tools called disturbance observers using fractional calculus, which estimate and eliminate disturbances in systems. The new approach outperformed existing methods, particularly when combined to handle highly fluctuating disturbance signals.