Output Information Research Articles

Torque Regularity is not Affected by the Nature of Visual Feedback During Hold Type of Isometric Contractions in Adults.

The literature that investigated the influence of visual feedback properties on the magnitude and temporal structure of variability suggests that increases in the precision of visual information of the torque output (through manipulation of visual gain and its intermittency) lead the neuromuscular system to produce torque in a more steady and complex manner. However, less is known about the influence of the nature of visual feedback on torque variability. The present study aimed to investigate the effect of two different feedback natures, one from the angular position and the other from the torque produced, on the magnitude and temporal structure of torque variability during submaximal hold isometric tasks. Twenty healthy and young participants performed a knee extension isometric task, consisting in sustaining an applied resistance equivalent to 40% of their Maximal Voluntary Isometric Contraction for 30 s with visual feedback from angular position (Hangle) and with visual feedback from torque (Htorque). The magnitude of torque variability was calculated through the coefficient of variation. Sample Entropy was used to analyze the temporal structure of torque fluctuations, i.e., torque complexity. We observed no significant differences between conditions (Hangle vs Htorque) in both magnitude (p = 0.117) and the temporal structure of torque fluctuations (p = 0.940). Our results demonstrated that torque regulation seems not to be affected by the nature of the visual feedback during hold submaximal isometric tasks, suggesting a participation of sensorimotor system due to the nature of the task. Researchers should take this into account to take methodological decisions when using hold submaximal isometric tasks to investigate possible changes in motor control.

A Deep-Reinforcement-Learning-Based Multi-Source Information Fusion Portfolio Management Approach via Sector Rotation

As a research objective in quantitative trading, the aim of portfolio management is to find the optimal allocation of funds by following the dynamic changes in stock prices. The principal issue with current portfolio management methods is their narrow focus on a single data source, neglecting the changes or news arising from sectors. Methods for integrating news data frequently face challenges with regard to quantifying text data and embedding them into portfolio models; this process often necessitates considerable manual labeling. To address these issues, we proposed a sector rotation portfolio management approach based on deep reinforcement learning (DRL) via multi-source information. The multi-source information includes the temporal data of sector and stock features, as well as news data. In terms of structure, in this method, a dual-layer reinforcement learning structure is deployed, comprising a multi-agent sector layer and a graph convolution layer. The former learns the trend of sectors, while the latter learns the connections between stocks in sectors, and the impact of news on sectors is integrated through large language models without manual labeling or fusing output information of other modules to provide the final portfolio management scheme. The results of simulation experiments on the Chinese and US (United States) stock markets show that our method demonstrates significant improvements over multiple state-of-the-art approaches.

Haptic Technology Interaction Framework in Engineering Learning: A Taxonomical Conceptualization

ABSTRACTInnovative technology helps students foster creative thinking and problem‐solving abilities by augmenting human sensing and enriching input and output information. New technology can incorporate haptic sensing features—a sensing modality for user operations. Learning with haptic sensing features promises new ways to master cognitive and motor skills and higher‐order cognitive reasoning tasks (e.g., decision‐making and problem‐solving). This study conceptualizes haptic technology within the human‐technology interaction (HTI) framework. It aims to investigate the components of haptic systems to define their impact on learning and facilitate understanding of haptic technology, including application development to ease entry barriers for educators. The research builds a haptic HTI framework based on a systematic literature review on haptic applications in engineering learning over the last two decades. The review utilizes the SALSA methodology to analyze relevant studies comprehensively. The framework outcome is a haptic HTI taxonomy to build visual representations of the explicit connection between the taxonomy components and practical educational applications (by means of heatmaps). The approach led to a robust conceptualization of HTI into a taxonomy—a structured framework encompassing categories for interaction modalities, immersive technologies, and learning methodologies in engineering education. The model assists in understanding how haptic feedback can be utilized in learning with technology experiences. Applying haptic technology in engineering education includes mastering fundamental science concepts and creating customized haptic prototypes for engineering processes. A growing trend focuses on wearable haptics, such as gloves and vests, which involve kinesthetic movement, fine motor skills, and spatial awareness—all fostering spatial and temporal cognitive abilities (the ability to effectively manage and comprehend significant amounts of spatial (how design components or resources are related to one another in the 3D space) and temporal (the logic in a process, such as the order, sequences, and hierarchies of the resources information). The haptic human‐technology interaction (H‐HTI) framework guides future research in developing cognitive reasoning through H‐HTI, unlocking new frontiers in engineering education.

Bipartite consensus of second‐order delayed unknown multi‐agent systems via aperiodically intermittent control

AbstractThis paper considers the bipartite consensus (BC) of second‐order delayed multi‐agent systems under signed graphs via aperiodically intermittent control (AIC). Firstly, to break the bottleneck of limited state information from the leader, a novel distributed observer is introduced based on the output information. Secondly, a new aperiodically intermittent controller is designed to achieve the consensus goal and reduce the control cost. By employing inequality techniques and Lyapunov stability theory, some sufficient conditions for ensuring BC of second‐order multi‐agent systems is ultimately obtained. Finally, the effectiveness of the theoretical analysis is demonstrated through a numerical example.

Fully distributed and attack-immune protocols for linear multiagent systems by linear time-varying feedback

Fully distributed and attack-immune protocols for linear multiagent systems by linear time-varying feedback

Large-scale flood risk assessment in data-scarce areas: an application to Central Asia

Abstract. The countries of Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan in Central Asia are highly prone to natural hazards, particularly floods, earthquakes, and landslides. The European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), created the programme Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) to advance disaster and climate resilience in the region. As part of the SFRARR project, the “Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia” was developed to achieve the project's objectives. This article presents the data, model, methodology, and results for the five Central Asian countries of the flood risk assessment, which represents the first high-resolution regional-scale transboundary risk assessment study in the area aiming to provide tools for decision-making. The output information will inform and enable the World Bank to initiate a policy dialogue. A fully probabilistic risk assessment for fluvial floods has been carried out for these countries to support regional and national risk financing and insurance applications, including potential indemnity and/or parametric risk financing solutions for a regional programme. A homogenised risk assessment methodology for the five countries and across multiple hazards (floods and earthquakes) and asset types has been adopted to obtain strategic financial solutions consistent across geographical areas and economic sectors. The largest relative (to the total exposed value) expected annual damages are found in Kazakhstan and Tajikistan, with values above 6 ‰. In the five considered countries, the largest relative expected annual damages by sector are found for the transport and agricultural sectors. Climate change is expected to have contrasting impacts, with increases in risk for some regions (the most severe increase is found in the Mangistauskaya region in Kazakhstan) and decreases for other regions (Lebap, Turkmenistan; Khatlon, Tajikistan; Samarkand, Uzbekistan; and Batken, Kyrgyz Republic).

Observer-Based Secure Consensus for Multiagent Systems Under Multimode DoS Attacks With Application to Power System.

This article studies the secure leader-follower consensus of nonlinear multiagent systems under multimode Denial-of-Service attacks. According to the topology characteristics after attacks, three kinds of different attack modes are introduced. Moreover, the common zero-topology attack is encompassed within the multimode attacks considered in this article. Due to the difficulty of directly obtaining the state of the system in some circumstances, a state observer is designed utilizing localized output information of the MASs to estimate the state. By applying suitable observer-based controller and Lyapunov method, some sufficient conditions are presented to ensure the observer-based secure consensus of the MASs under multimode DoS attacks. Furthermore, the obtained results can be extended to linear MASs under DoS attacks. Finally, two practical examples on power system are provided to demonstrate the validity of the derived theoretical results.

Errors-in-variables model fitting for partially unpaired data utilizing mixture models

We introduce a general framework for regression in the errors-in-variables regime, allowing for full flexibility about the dimensionality of the data, observational error probability density types, the (nonlinear) model type and the avoidance of ad-hoc definitions of loss functions. In this framework, we introduce model fitting for partially unpaired data, i.e., for given data groups the pairing information of input and output is lost (semi-supervised). This is achieved by constructing mixture model densities, which directly model the loss of pairing information allowing inference. In a numerical simulation study linear and nonlinear model fits are illustrated as well as a real data study is presented based on life expectancy data from the world bank utilizing a multiple linear regression model. These results show that high quality model fitting is possible with partially unpaired data, which opens the possibility for new applications with unfortunate or deliberate loss of pairing information in data.

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant focus of defense. In this work, we propose a deep learning defense known as multi-source adversarial perturbations elimination (MAPE) to counter diverse transferable attacks. MAPE comprises the single-source adversarial perturbation elimination (SAPE) mechanism and the pre-trained models probabilistic scheduling algorithm (PPSA). SAPE utilizes a thoughtfully designed channel-attention U-Net as the defense model and employs adversarial examples generated by a pre-trained model (e.g., ResNet) for its training, thereby enabling the elimination of known adversarial perturbations. PPSA introduces model difference quantification and negative momentum to strategically schedule multiple pre-trained models, thereby maximizing the differences among adversarial examples during the defense model’s training and enhancing its robustness in eliminating adversarial perturbations. MAPE effectively eliminates adversarial perturbations in various adversarial examples, providing a robust defense against attacks from different substitute models. In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1% on CIFAR-10 and over 71.5% on Mini-ImageNet, demonstrating state-of-the-art performance.

A Comparison of Large Language Model Powered Search Tools Reveal Differences in Output Quality, Information Transparency, and Accessibility for Mohs Micrographic Surgery Inquiries.

A Comparison of Large Language Model Powered Search Tools Reveal Differences in Output Quality, Information Transparency, and Accessibility for Mohs Micrographic Surgery Inquiries.

Evaluation of Inventory Accounting Information Systems Using Pieces Method: A Study on SIMEDi’s Application

This study aims to evaluate the SIMEDi inventory accounting information system in Perumda Perkebunan Kahyangan Jember. The method used in this study is a case study with a PIECES analysis approach. Data collection techniques were carried out by conducting interviews with the board of directors and staff who have access to SIMEDi. The PIECES approach involves six evaluation variables, namely performance, information, economic, control and security, efficiency and service. The results of the evaluation showed that SIMEDi meets the six PIECES variables, namely: all SIMEDi features have been running well (performance variables), the information produced by SIMEDi has met reliable input process output (information variables), the benefits generated from the implementation of SIMEDi are greater than the costs incurred (economic variables), SIMEDi has provided adequate system control to maintain the reliability of the information produced (control and security variables), SIMEDI makes inventory management at Perumda Kahyangan more efficient (efficiency variables), and SIMEDi is very user friendly and easy to operate (service variables). The SIMEDi Inventory Accounting Information System provides convenience and reliability for Perumda Perkebunan Kahyangan in managing inventory, especially for making appropriate and accurate decisions.

Output Formation Containment of Heterogeneous Singular Multiagent Systems Using Adaptive Protocols

ABSTRACTIn this study, the output formation containment control problem for heterogeneous singular multiagent systems is investigated. The purpose of this study is to drive the output of the leader agents to reach a time‐varying formation and take containment action for the follower agents. First, a novel distributed adaptive protocol is designed that relies on the relative output information of agents without measuring and transmitting their states information. Then, the formation containment control problem can be simplified into an admissible analysis of coupled nonlinear singular systems by constructing a model transformation. The output formation control conditions related to the spectral radius of the weighted adjacency matrix are obtained based on the bounded real lemma and admissible analysis. In addition, the adaptive protocols based on the state of the virtual leader are proposed, which can drive the agents to achieve formation containment control more quickly when the state of the virtual leader is known. Finally, the simulation results verify the feasibility of the output formation containment control.

The Pros and Cons of Chatgpt Application in Higher Education - How to Be A "Middleman" Correctly

Since the rapid development of the information age, artificial intelligence has been widely used in various fields. Among them, ChatGPT, as a representative application, has become a hot topic in higher education in recent years. Artificial intelligence tools have significantly improved the efficiency and quality of higher education, which is conducive to developing the "innovative education " model, decomposing educational tasks, and developing multi-faceted in-depth teaching. However, there are risks in the application of ChatGPT in higher education. Mainly in terms of information accuracy. ChatGPT may find it difficult to guarantee the absolute accuracy of all output information due to the extensiveness and complexity of data sources. In addition, students' excessive dependence on ChatGPT may weaken their motivation and ability for autonomous learning and affect their personal growth and development. At the same time, privacy and security issues are also aspects that cannot be ignored. Finally, technical limitations and stability issues also restrict the effectiveness of ChatGPT. Technical failures or updates may bring unnecessary interference and risks to the educational process. Therefore, how to use artificial intelligence tools correctly, whether ChatGPT is more harmful than beneficial to higher education, and the opportunities and challenges it faces in higher education all require "middlemen" to make correct assessments.

Research on Digital Terrain Construction Based on IMU and LiDAR Fusion Perception

To address the shortcomings of light detection and ranging (LiDAR) sensors in extracting road surface elevation information in front of a vehicle, a scheme for digital terrain construction based on the fusion of an Inertial Measurement Unit (IMU) and LiDAR perception is proposed. First, two sets of sensor coordinate systems were configured, and the parameters of LiDAR and IMU were calibrated. Then, a terrain construction system based on the fusion perception of IMU and LiDAR was established, and improvements were made to the state estimation and mapping architecture. Terrain construction experiments were conducted in an academic setting. Finally, based on the output information from the terrain construction system, a moving average-like algorithm was designed to process point cloud data and extract the road surface elevation information at the vehicle’s trajectory position. By comparing the extraction effects of four different sliding window widths, the 4 cm width sliding window, which yielded the best results, was ultimately selected, making the extracted road surface elevation information more accurate and effective.

Groupy: An Open-Source Toolkit for Molecular Simulation and Property Calculation.

In this work, an open-source, versatile, and flexible code named Groupy is present for calculating various molecular properties and preparing input files of molecular simulation software such as Gaussian. This code requires only SMILES as input, but can output many new useful data and files in multiple formats. The output information is clear and easy to read. The tips to the users are very detailed and easy to follow when using. Message passing interface (MPI) parallelization is supported to reduce computing time when the properties of a large number of molecules are calculated. Groupy not only supports the calculation of molecular properties using the traditional group contribution method, but also directly outputs the group-contribution-style molecular fingerprints for machine learning. The code has strong extensibility, which can be used as an external library to build other programs. We hope that Groupy brings great convenience to both computational and experimental chemists in their daily research. The code of Groupy can be freely obtained at https://github.com/47-5/Groupy.

Optimal control of grid-connected overvoltage of distributed photovoltaic power generation based on cluster division

To address the overvoltage problem caused by the reverse flow of current when a high proportion of distributed photovoltaic (PV) is connected to the distribution network, this paper proposes a grid-connected voltage regulation control strategy based on the cluster division of the Distributed Model Predictive Control (DMPC) algorithm. Firstly, the overvoltage responsibility of each node is calculated using the Shapley value method. This is combined with k-means clustering to achieve effective cluster division, enabling dynamic adjustment of the active and reactive power of photovoltaic power generation units to stabilize regional voltage. Secondly, a group grid-connected voltage control strategy is introduced. This strategy controls the active and reactive power outputs by integrating real-time power output and voltage information from PV generating units in the region with the DMPC algorithm, ensuring overall voltage stability of the grid-connected system. Finally, actual overvoltage data from a 10 kV distribution line in the Dingxi power grid, Gansu Province, is used to verify that under the proposed control strategy, PV grid-connected overvoltage nodes are maintained within 1.06 p.u. The control effect is improved by a margin of 0.05 compared to traditional control methods. This demonstrates the effectiveness of the grouped grid-connected voltage regulation control strategy, achieving smoother voltage regulation performance in distributed PV grid-connected systems.

Adaptive Dynamic Event-Triggered Distributed Output Observer for Leader-Follower Multiagent Systems Under Directed Graphs.

Leader-follower consensus problem for multiagent systems (MASs) is an important research hotspot. However, the existing methods take the leader system matrix as a priori knowledge for each agent to design the controller and use the leader's state information. In fact, only the output information may be available in some practical applications. On this basis, this article first designs a novel adaptive distributed dynamic event-triggered observer for each follower to learn the minimum polynomial coefficients of the leader system matrix instead of the leader system matrix. The proposed method is scalable and suitable for large-scale MASs and can reduce the information transmission dimension in observer design. Then, an adaptive dynamic event-triggered compensator based on the observer and leader output information is designed for each follower, thereby solving the leader-follower consensus problem. Finally, several simulation examples are given to verify the effectiveness of the proposed scheme.

Eye Gaze Guided Cross-Modal Alignment Network for Radiology Report Generation.

The potential benefits of automatic radiology report generation, such as reducing misdiagnosis rates and enhancing clinical diagnosis efficiency, are significant. However, existing data-driven methods lack essential medical prior knowledge, which hampers their performance. Moreover, establishing global correspondences between radiology images and related reports, while achieving local alignments between images correlated with prior knowledge and text, remains a challenging task. To address these shortcomings, we introduce a novel Eye Gaze Guided Cross-modal Alignment Network (EGGCA-Net) for generating accurate medical reports. Our approach incorporates prior knowledge from radiologists' Eye Gaze Region (EGR) to refine the fidelity and comprehensibility of report generation. Specifically, we design a Dual Fine-Grained Branch (DFGB) and a Multi-Task Branch (MTB) to collaboratively ensure the alignment of visual and textual semantics across multiple levels. To establish fine-grained alignment between EGR-related images and sentences, we introduce the Sentence Fine-grained Prototype Module (SFPM) within DFGB to capture cross-modal information at different levels. Additionally, to learn the alignment of EGR-related image topics, we introduce the Multi-task Feature Fusion Module (MFFM) within MTB to refine the encoder output information. Finally, a specifically designed label matching mechanism is designed to generate reports that are consistent with the anticipated disease states. The experimental outcomes indicate that the introduced methodology surpasses previous advanced techniques, yielding enhanced performance on two extensively used benchmark datasets: Open-i and MIMIC-CXR.

Operational interpretation of the Choi rank through exclusion tasks

The Choi state is an indispensable tool in the study and analysis of quantum channels. Considering a channel in terms of its associated Choi state can greatly simplify problems. It also offers an alternative approach to the characterization of a channel, with properties of the Choi state providing novel insight into a channel's behavior. The rank of a Choi state, termed the Choi rank, has proven to be an important characterizing property, and here, its significance is further elucidated through an operational interpretation. The Choi rank is shown to provide a universal bound on how successfully two agents, Alice and Bob, can perform an entanglement-assisted exclusion task. The task can be considered an extension of superdense coding, where Bob can only output information about Alice's encoded bit string with certainty. Conclusive state exclusion, in place of state discrimination, is therefore considered at the culmination of the superdense coding protocol. In order to prove this result, a necessary condition for conclusive k-state exclusion of a set of states is presented in order to achieve this result, and the notions of weak and strong exclusion are introduced. Published by the American Physical Society 2024

Optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response

AbstractTo reduce phenomenon of abandoning wind and photovoltaic power, improve the limitations of traditional methods in dealing with uncertainty of wind and photovoltaic power and system planning, and improve the optimal configuration of resources, an optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response is proposed. Firstly, using probability distribution information of wind and photovoltaic power output, the distance between actual probability distribution and forecast probability distribution is constrained based on the 1‐norm and ∞‐norm. A fuzzy set considering uncertainty probability distribution is constructed, and a two‐stage distributed robust planning model is established. The first stage involves optimizing joint system capacity for scenarios with the lowest probability of wind and photovoltaic power; the second stage builds on capacity optimization scheme from the first stage and aims to minimize operating costs through simulation optimization. Secondly, column and constraint generation is used to solve the model. Finally, constructing an example based on actual data from a power grid in Northeast China for simulation and analysis, the results show that the method achieves a balanced optimization of robustness and economy, effectively reduces carbon emissions and improves ability of the system to consume wind and photovoltaic power.