Patrol robots in hospital wards are expected not only to ensure the safety of their movements, but also to perform gait measurement as part of their monitoring functions. In this study, we present a system that enable...
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Patrol robots in hospital wards are expected not only to ensure the safety of their movements, but also to perform gait measurement as part of their monitoring functions. In this study, we present a system that enables a patrol robot to perform a 5-meter walk test (5mWT) on passing pedestrians in a corridor. The pedestrian's position and velocity are measured sequentially, and the robot's trajectory is generated based on the results using a nonlinear programming method, followed by Model Predictive Control tracking. This allows for adaptive gait measurement during passing interactions, taking into account arbitrary pedestrian trajectories. Furthermore, a parameter estimation method tailored to the characteristics of the acquired data, namely whole-body, continuous and multi-view measurements, is proposed. Through simulation and experiments, we confirmed the feasibility of the measurement operation and accuracy of the derived gait parameters. The proposed method can not only be used anywhere, but also measure gait over a long distance with high accuracy using a single sensor. Moreover, it could contribute to the realization of gait measurement without pedestrian's tension.
In recent years, the workload of healthcare workers in hospitals has increased, and there has been a demand for autonomous patrolling instead of nurses and gait measurements of patients in hospital wards. In this stud...
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ISBN:
(数字)9783031448515
ISBN:
(纸本)9783031448508;9783031448515
In recent years, the workload of healthcare workers in hospitals has increased, and there has been a demand for autonomous patrolling instead of nurses and gait measurements of patients in hospital wards. In this study, a novel motion-planning method is proposed to enable a mobile robot to measure the gait of passing pedestrians while patrolling wards. In nonlinear programming, the feasibility of the gait measurement is evaluated as an object function, and constraints on the robot kinematics, environment, collision avoidance, and target position and arrival time are considered in the constraints. The optimization variables are the trajectory of the robot's position, velocity, and acceleration. This schematization guarantees that the control input, which is the solution to this problem, satisfies all constraints and is optimized to realize gait measurement. The results of the simulation and experiment demonstrated the efficiency and evolvability of the proposed method.
Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction co...
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Improving prediction computation for time series analysis is still a challenge. Finding a method that combines the benefits of different methodologies is still an open problem. Besides the very efficient prediction combination techniques proposed, there is still a lack of procedures that jointly consider error measure combinations and model constraints. In this work, we propose anew forecast combination procedure based on multi-criteria methods that allows the assignment of weights to different error measures in the objective function and the incorporation of constraints. Areal case from the pharmaceutical industry for the sale of a probiotic product is presented to illustrate the performance of the proposal. This method is capable of considering different error measures and non distance based errors, is enriched by the consideration of constraints that consider desirable properties of the solution and is robust with respect to different time series characteristics such as trends, seasonality, etc. Results shows similar accuracy to the best known forecasting methods to date.
In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma...
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In recent years, intuitionistic fuzzy theory has gained significant attention for its ability to handle uncertainty through both membership and non-membership degrees. This paper presents a novel modification to gamma-operator technique by introducing distinct gamma-operators for membership and non-membership functions to tackle intuitionistic fuzzy environment. The proposed technique is rigorously validated through the proof of relevant theorems that demonstrate its capability to derive efficient solutions for multi-objective nonlinear programming problems, where all parameters are represented as triangular intuitionistic fuzzy numbers. To elucidate the proposed technique, an illustrative example is presented. Furthermore, a comparative study with existing techniques is conducted, which highlights the superior performance of the proposed method. Finally, an application in the agriculture sector demonstrates the practical relevance and effectiveness of the proposed method in real-world scenarios.
Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore,...
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Recently, due to global climate change and population growth, environmental protection has become more interested. Water is the main critical issue because it is the most significant environmental resource. Therefore, this study introduces a novel approach to examine, modeling, and addressing the monitoring of water quality (WQ) critical scenario related to unexpected extreme variations of crucial indicators (UEVCI). Therefore, this research integrates ensemble machine learning (EML) techniques with Non-linear programming (NLP) and Simulated annealing algorithm (SAA) to develop an optimal weighted ensemble models. New development models were nonlinear-programmed ensemble machine learning (NLEML) and simulated annealing ensemble machine learning (SAEML). Besides, we developed least-squared boosted regression tree (LsBRT), artificial neural network (ANN), and multiple linear regression (MLR) models individually to compare the performance of new ensemble models. The South Platte River Basin in Colorado, USA was the study region. The initial dataset was extracted through the United States Geologic Survey (USGS) from 2023 to 2024. Preprocessing approaches such as cleaning missing data (CMD), cleaning outlier data (COD), and k-fold cross validation (KFCV) with k = 5 were used to prepare the dataset. The final dataset was utilized to examine variations of essential parameters that affect water health and quality, including the power of hydrogen (pH) and dissolved oxygen (DO). The results showed that the NLEML provided the most accurate results in estimating fluctuation of pH parameter with an R2 coefficient of 0.85. Also, the NLEML estimated the variance of the DO parameter with an R2 equal to of 0.79, resulting in an outperforming simulation.
This paper extends the concept of higher-order search directions within interior point methods to convex nonlinear programming. This includes the mathematical framework needed to compute the higher-order derivatives. ...
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This paper extends the concept of higher-order search directions within interior point methods to convex nonlinear programming. This includes the mathematical framework needed to compute the higher-order derivatives. The paper also highlights some special cases where the computation of these higher-order derivatives is simplified and a dimensional lifting procedure for transforming a large number of general nonlinear problems into one of these more efficient forms. The paper further describes the algorithmic development required to employ these higher-order search directions in a practical algorithm. Computational results are presented for a large number of test problems, highlighting higher-order methods' strong potential for decreasing iteration count and their case-by-case potential for decreasing CPU time.
This article considers the problem of designing a continuous-time dynamical system that solves a constrained nonlinear optimization problem and makes the feasible set forward invariant and asymptotically stable. The i...
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This article considers the problem of designing a continuous-time dynamical system that solves a constrained nonlinear optimization problem and makes the feasible set forward invariant and asymptotically stable. The invariance of the feasible set makes the dynamics anytime, when viewed as an algorithm, meaning it returns a feasible solution regardless of when it is terminated. Our approach augments the gradient flow of the objective function with inputs defined by the constraint functions, treats the feasible set as a safe set, and synthesizes a safe feedback controller using techniques from the theory of control barrier functions. The resulting closed-loop system, termed safe gradient flow, can be viewed as a primal-dual flow, where the state corresponds to the primal variables and the inputs correspond to the dual ones. We provide a detailed suite of conditions based on constraint qualification under which (both isolated and nonisolated) local minimizers are asymptotically stable with respect to the feasible set and the whole state space. Comparisons with other continuous-time methods for optimization in a simple example illustrate the advantages of the safe gradient flow.
Tendon sheath mechanism (TSM) is an essential mechanical element for the implementation of flexible endoscopic systems owing to its small volume and simple structure. However, nonlinear characteristics, such as backla...
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Tendon sheath mechanism (TSM) is an essential mechanical element for the implementation of flexible endoscopic systems owing to its small volume and simple structure. However, nonlinear characteristics, such as backlash, hysteresis and friction occur when employing such a component. In this study, we formulate a Preisach hysteresis model consisting of elementary hysteresis operators. Subsequently, we propose a compensation algorithm that repeatedly and sequentially solves a nonlinear optimization problem online, producing an inverse control signal for the desired output at every time step, compensating the nonlinear effects of TSM. The results indicate that the presented model and control scheme are promising for motion control in any application utilizing TSM.
To address the interior negative acoustic effects (NAEs) generated in vehicular sound cavities during acoustic modulation, this investigation proposes a targeted reproduction approach oriented towards the interior sou...
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To address the interior negative acoustic effects (NAEs) generated in vehicular sound cavities during acoustic modulation, this investigation proposes a targeted reproduction approach oriented towards the interior sound field based on nonlinear programming. For a given vehicular sound cavity, the sound source locations matching the acoustical transmission responses in vehicle environment is determined, and NAEs composed of scattering and reflection effects are interpreted and calculated. By the numerical equivalence of three reproductive challenges, the targeted sound field reproduction (SFR) based on nonlinear programming is formulated. Combined with the practical loudspeaker properties, the influence of NAEs on reproduced sound fields and its mitigation are analyzed. An experimental verification platform corresponding to vehicular sound cavity is constructed, so the different performances with the free fields and vehicle driving conditions are investigated, respectively. Simulation and measurement results both demonstrate that the proposed method not only achieves brilliant SFR performance, but also effectively achieves NAEs mitigation through targeted compensation mechanism. This research, starting from the acoustical characteristic of vehicular sound cavity and the equivalence calculation for interior NAEs, can provide technical support for high-quality acoustical performances in vehicle environment.
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