Operator's Intention Research Articles

Assessment of surgical revascularization completeness with quantitative myocardial blood flow distribution derived from CCTA in severe coronary artery disease

Abstract Background Complete revascularization remained the "holy grail" in the treatment of patients with severe coronary artery disease (CAD). However, there is no universally accepted definition of complete revascularization after coronary bypass graft surgery (CABG). The current assessment methods are visual, categorical, and biased by the operator's intention. Moreover, no method to date provides an individualized quantification of residual ischemia burden. Purpose We aim to provide an individualized method to assess the completeness of surgical revascularization through quantitative myocardial blood flow distribution derived from coronary computed tomography angiography (CCTA) that could reflect the actual functional ischemia burden after CABG. Methods Patients with 3VD and/or left main CAD were enrolled in the first-in-human FAST TRACK CABG trial for surgical revascularization guided solely by CCTA and fractional flow reserve derived from CCTA (FFRCT). Following CABG, the study protocol mandated a 30-day follow-up CCTA, which provides graft patency and topographical adequacy of the bypass graft anastomoses. The pre- and post-CABG CCTA were analyzed. The FFRCT value and percent myocardial blood flow distribution (%MBF) were computed for all 16 SYNTAX score segments using the validated method of Keulards et al(1). On pre-CABG CCTA, the myocardium located distal to the site where the FFRCT value dropped below 0.80 on the vessel centerline was considered ischemic, and the %MBF of the subtended myocardium was summed into the total percent ischemic myocardium (Figure 1). Following CABG, the segment was considered adequately revascularized when a non-narrowed graft was anastomosed distally to the site of FFRCT≤0.8 on the pre-operative CCTA. The change in percent ischemic myocardium and the residual ischemic burden were calculated for each patient (Figure 2). Results CCTA, FFRCT, and %MBF were obtained pre- and post-CABG in 96 patients, and the percent ischemic myocardium was computed per patient. At baseline, the average percent ischemic myocardium was 72.0(19.1)%. Post-CABG, the residual percent ischemic myocardium was 13.6(15.1)%. Residual percent ischemic myocardium <10% was achieved in 44 patients (44.8%). The first diagonal branch(n=13), distal LCX(n=12), and the first obtuse marginal branch(n=10) were the most frequent segments not revascularized, which subtended an average MBF of 12.2(3.5)%, 16.7(8.4)%, and 11.3(7.3)%, respectively. The main reason for inadequate revascularization was surgical deferral, with graft occlusion accounting for only 13.6% of the residual ischemic segments. Conclusion Percent ischemic myocardium and the completeness of surgical revascularization can be assessed with CCTA-derived %MBF. This novel method allows clinicians to assess the individualized ischemia burden and the completeness of revascularization that could be incorporated into personalized CABG planning.Figure 1Figure 2

Re-enactment and embodied resonance in episodic memory: reconciling phenomenological approaches and constructive theories

AbstractThis paper investigates the embodied dimensions of episodic memory through a phenomenological analysis. Contemporary philosophical theories understand episodic memory as a mental representation of a past event ‘happening in the head’. A philosophical account that seemingly supports an embodied understanding of episodic memory comes from phenomenology. Phenomenology has traditionally understood episodic memory in terms of presentification, which implies a reproduction of the elapsed portion of the consciousness lived during the foreground experience, replicating the previous perceptual activity. However, this appears to be incoherent with the constructive theories of episodic memory, which highlight how subjects shift perspectives in memory or include information not present in the foregoing experience. Thus, according to these contemporary theories, episodic memory is unlikely to imply a replay of the past conscious experience. I argue that if we investigate the embodied dimensions, then it is possible to reconcile the phenomenological approach and the constructive theories. More precisely, I ground the investigation on the analysis of operative intentionality and its role in comparing procedural and episodic memory. I argue that two embodied features qualify the reproductive modification of consciousness: the flexible re-enactment of the previous operative intentionality and the pre-reflective mode of embodied resonance with an agent I am no longer.

Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models-a kinematic extrapolation algorithm and a Random Forest machine learning algorithm-when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device.

Design on temporal-spatial Transformer model for air target intention recognition

The battlefield changes rapidly in information warfare. The battlefield situation data presents massive and diversified characteristics, which makes it increasingly difficult to identify the operational intent of air targets based on expert experience. In combination with the current state-of-the-art intelligent methods, the Transformer model is studied and introduced into the field of air target intent recognition for the first time, and a new intent recognition method temporal-spatial transformer(TST) is designed, which can effectively mine the deep feature information in the temporal and spatial domains of battlefield situational data to improve the accuracy of air target combat intention recognition. In addition, a comparative study of the four currently advanced neural network intention recognition methods shows that TST achieved outstanding performance in all indicators and outperformed all compared neural network models. TST method not only has excellent accuracy but also extremely fast convergence rate, which allows it to quickly capture key information from battlefield situation data for intention recognition.

Experimental guidance and feedback via operation intention prediction with effect analysis in chemistry labs

Experimental guidance and feedback via operation intention prediction with effect analysis in chemistry labs

Usefulness of Preoperative Simulation: Skull Base Approach

Preoperative simulation images creates an accurate visualization of a surgical field. The anatomical relationship of the cranial nerves, arteries, brainstem, and related bony protrusions is important in skull base surgery. However, an operator's intention is unclear for a less experienced neurosurgeon. Three-dimensional(3D)fusion images of computed tomography and magnetic resonance imaging created using a workstation aids precise surgical planning and safety management. Since the simulation images allows to perform virtual surgery, a déjà vu effect for the surgeon can be obtained. Additionally, since 3D surgical images can be used for preoperative consideration and postoperative verification, discussion among the team members is effective from the perspective of surgical education for residents and medical students. Significance of preoperative simulation images will increase eventually.

Offensive Cyber Operations and State Power: Lessons from Russia in Ukraine

Offensive cyber operations are an increasingly important tool of authoritarian statecraft, used in the pursuit of foreign policy objectives and functioning as an expression of state power. Our article informs Canadian cybersecurity policy by pulling lessons from Russia's use of cyber aggression against Ukraine between 2014 and 2022. Our analysis explores the nexus between Russian cyber operations and foreign and domestic policy, the relationship between private and public cybersecurity organizations, and the purported intent and effectiveness of Russian cyber operations.

Teleoperated Grasping Using Data Gloves Based on Fuzzy Logic Controller.

Teleoperated robots have attracted significant interest in recent years, and data gloves are one of the commonly used devices for their operation. However, existing solutions still encounter two challenges: the ways in which data gloves capture human operational intentions and achieve accurate mapping. In order to address these challenges, we propose a novel teleoperation method using data gloves based on fuzzy logic controller. Firstly, the data are collected and normalized from the flex sensors on data gloves to identify human manipulation intentions. Then, a fuzzy logic controller is designed to convert finger flexion information into motion control commands for robot arms. Finally, experiments are conducted to demonstrate the effectiveness and precision of the proposed method.

Do great apes switch perspectives? Husserl, Tomasello, and operative intentionality

Do great apes switch perspectives? Husserl, Tomasello, and operative intentionality

Intention recognition-based human–machine interaction for mixed flow assembly

Mixed-flow assembly provides a highly adaptable solution for production processes, catering to the extensive customization requirements in product manufacturing. Human–machine interaction (HMI) can significantly enhance the efficiency, flexibility, and responsiveness of mixed-flow production lines by enabling operators to collaborate with machines. However, current HMI research primarily focuses on temporal or spatial interaction, where tasks are completed independently by the operator and machine in a predetermined sequence, with emphasis on collision avoidance in a shared workspace. Research on spatial–temporal HMI is currently lacking. This paper addresses this gap through three comprehensive steps. Firstly, we develop an intention recognition model based on Gaussian mixture distribution to effectively identify the user’s operational intention. Subsequently, we design a hybrid industrial communication architecture to facilitate intent sharing among various production elements. Finally, an empirical study is conducted to assess the impact of different forms of HMI on assembly efficiency and error rates. Our experimental results demonstrate that spatial–temporal HMI can enhance assembly efficiency by approximately 15% and reduce the defective rate. In future studies, the proposed spatial–temporal cooperation platform could integrate additional perception technologies to alleviate assembly fatigue and improve operators’ well-being.

Recognition of Grasping Patterns Using Deep Learning for Human-Robot Collaboration.

Recent advances in the field of collaborative robotics aim to endow industrial robots with prediction and anticipation abilities. In many shared tasks, the robot's ability to accurately perceive and recognize the objects being manipulated by the human operator is crucial to make predictions about the operator's intentions. In this context, this paper proposes a novel learning-based framework to enable an assistive robot to recognize the object grasped by the human operator based on the pattern of the hand and finger joints. The framework combines the strengths of the commonly available software MediaPipe in detecting hand landmarks in an RGB image with a deep multi-class classifier that predicts the manipulated object from the extracted keypoints. This study focuses on the comparison between two deep architectures, a convolutional neural network and a transformer, in terms of prediction accuracy, precision, recall and F1-score. We test the performance of the recognition system on a new dataset collected with different users and in different sessions. The results demonstrate the effectiveness of the proposed methods, while providing valuable insights into the factors that limit the generalization ability of the models.

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver’s operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel.

Strategic Deconfliction of Unmanned Aircraft Based on Hexagonal Tessellation and Integer Programming

Unmanned aircraft systems service suppliers adhere to interoperability standards that require unmanned aircraft operators to submit an operational intent, which describes the planned flight path in four-dimensional space. To ensure fairness, the central database follows a first-come, first-served approach, accepting new operational intents as long as they do not conflict with any active ones. However, creating a viable operational intent is challenging due to moving obstacles. This paper introduces an innovative optimization-based procedure to automate the intent filing process. It utilizes a stacked hexagonal tessellation to model the airspace, offering adjustable granularity. Path finding is accomplished using integer programming on the hex grid. The integer program is solved on a grid canvas that includes only necessary cells, striking a balance between computational efficiency and optimality. Simulation experiments demonstrate the procedure’s effectiveness in generating feasible trajectories, even in scenarios with dense, omnidirectional air traffic. This procedure has the potential to become the foundational software core for low-altitude air traffic management systems, providing strategic deconfliction and constraint management services.

Intentional Understanding and Human-Computer Collaboration: A Smart Pen for Solid Geometry Teaching

The current teaching mode in geometry mainly focuses on two-dimensional levels, and the teaching tools utilized are static and not interactive. This paper proposes the use of a smart pen for three-dimensional geometry experimental teaching and a multimodal intention understanding and human-computer collaboration algorithm for the smart pen. The primary innovations of this paper lie in the development of a smart pen and a virtual platform for geometry education tailored for geometry experimental instruction. This system can promptly perceive and comprehend user behavior in real-time. Furthermore, a standardized topological equivalence model is proposed as the basis for a point selection strategy. By establishing correspondence between the modeled point selection model and the actual operation scene, the behavioral intent imposed on the model is applied to the operation object of the actual scene. Additionally, CNN-based and information entropy-based multimodal fusion intention understanding models are proposed for different input modalities to capture the operational intention of users by fusing their multimodal input data. The algorithm further improves the accuracy rate through an error correction mechanism based on implicit interaction to achieve better human-computer collaboration. The algorithm proposed in this paper has resulted in a 0.47-second improvement in point selection average time and achieved an intent understanding accuracy of 98.21%. This improvement leads to better fault tolerance and fluency during human-computer interaction, reduces the cognitive load on the user, and improves the overall user experience.

Fixed-time personalized variable gain tracking control for teleoperation systems with time varying delays

In this paper, a fixed-time personalized variable gain control (FTPGC) scheme is developed for a class of teleoperation systems. Efforts in this paper seek to improve the tracking performance under different operation intentions for teleoperation systems in the presence of time varying delays and uncertainties. By virtue of nonsmooth analysis, a fixed-time unknown observer (FUO) is firstly devised for the teleoperation systems such that the matched/mismatched unknowns can be observed within a fixed time. Meanwhile, with the help of an auxiliary function, a novel nonsingular fixed-time control framework is subsequently constructed without using any piecewise continuous switching functions. Moreover, the FUO and a variable gain involving electromyographic are incorporated into the fixed-time control framework, and thereby resulting the FTPGC scheme to improve trajectory tracking performance in the presence of unknowns and different operation intentions. The fixed-time stability of the entire FTPGC scheme is derived by rigorously theoretical analysis. Finally, comprehensive experiments and comparisons are conducted to demonstrate the effectiveness and feasibility of the proposed scheme.

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

Confirmation Method for Flight Operators’ Interaction Based on the Improved Lightweight DenseNet

Rapid and accurate confirmation of operational intent can improve the efficiency of flight operators in handling sudden emergencies. A lightweight DenseNet flight operator blink interaction confirmation method based on visual gaze is proposed to address the issues of low accuracy and long-time consumption in traditional methods for confirming operational intentions. After determining whether the visual gaze of the flight operator is located at the confirmation button, this method uses two consecutive blinks to confirm the intention of the operation. When performing blink recognition, an improved lightweight DenseNet network model is used, which only uses one block and introduces attention SEBlocks in its [Formula: see text]1 and [Formula: see text]2 layers. Through testing experiments on our own dataset with open and closed eye images, it has been shown that this method has higher accuracy and lower loss compared to the classical DenseNet for open and closed eye recognition. The interactive experiment of flight operation intention confirmation showed that this method requires a shorter time for operation intention confirmation.

An effective process design intent inference method of process data via integrating deep learning and grammar parsing

With the constantly generation of process data in industries, process knowledge reuse and learning from process dataset has shown promising potential in machining process planning. However, existing methods cannot effectively extract different granularity process intents, and thus the process data cannot be effectively utilized. In this paper, an effective process design intent inference method of process data via integrating deep learning and grammar parsing is proposed. First, based on the projection of cutter location segments, an association approach between machining operations and machining features is proposed to extract the process intent of each machining operation. Then, a deep learning based working step process intent sequence decision-making model is proposed to estimate the probability distributions of alternative working step process intents at each time. Moreover, according to the working step process intent sequences of existing parts in an industry, the process knowledge And-Or graph (PK-AOG) is constructed by introducing the probabilistic grammar graph model. Finally, the optimal process intents of each working step are jointly obtained based on grammar parsing by taking the PK-AOG as a guide. In the experiments, the effectiveness of our approach is validated by developing a prototype system, and the results show that our approach is practical in process intents extraction.

A MULTI-MODAL INTENTION INTEGRATION METHOD FOR UNMANNED AERIAL VEHICLES BASED ON THE COMBINATION OF BIOELECTRICAL SIGNALS AND CONVENTIONAL HUMAN–COMPUTER INTERACTION

With the rapid development of intelligent information technology, human–computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human–computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human–computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human–computer interaction and has great development potential. The characteristics of human–computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human–computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious advantages.

Fuel-Saving-Oriented Collaborative Driving Strategy for Commercial Vehicles Based on Driving Style Recognition

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver’s operational intentions while surpassing the driver’s fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver’s intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers.