Conventional Constraints Research Articles

Intuitive Remote Robotic Nasal Sampling by Orientation Control With Variable RCM in Limited Space

When performing polymerase chain reaction testing, collecting nasopharyngeal swabs from patients is necessary. Hence, the development of a noncontact specimen collection system is desired to avoid the risk of secondary infection from direct contact between the medical personnel and patients. This study aims to develop a master-follower-type remote specimen collection system to achieve infection protection through adequate isolation. A teleoperated robot is used for specimen collection work instead of a human; the teleoperated robot moves remotely, and a cotton swab is grasped and inserted into the nostril by the robot. A remote center of motion (RCM) mechanism was implemented to prevent the cotton swabs from making excessive contact with the nostril during specimen collection. Further, a variable RCM constraint that combines safety and operability is proposed as the conventional RCM control modification. The evaluation results indicated that the variable RCM restraint allowed a quicker and safer collection of specimens than the conventional RCM constraint.

Understanding IoHT and Edge/Fog Computing Solutions for Smart In-Home Remote Healthcare

Healthcare information systems have been dominated by cloud technology and the Internet of Things (IoT) for decades today. In some urgent scenarios, we reveal a prevailing architectural framework that is based on fog/edge optimal computing approaches smart in-home remote healthcare solutions and architectures and recognize the challenges and requirements of IoHT devices for diverse utilization instances. Even with these upsides, conventional centralized access constraint confronts privacy problems and patient health data security. This study likewise constructs a “blockchain-enabled edge that computes” mechanism, through which smart contracts with the consensus protocol produced by Edge Intelligent Server are deployed to secure privacy topics and balance scalability in trustless surroundings. We expect this paper to be a significant guideline for the subsequent elaboration of fog/edge-based systems that compute solutions for smart in-home remote healthcare IoT applications. There will be a change of paradigm from “hospital-based” to “distributed patient in-home healthcare”.

Quantum entanglement inspired hard constraint handling for operations engineering optimization with an application to airport shift planning

Constraint handling, especially hard constraint handling, is a critical issue for discrete constrained optimization problem solving in operations engineering. The performance of conventional constraint handling techniques, such as penalty functions and repair operators, are susceptible to problem complexity and scale. The recent dawning of quantum computation has been recognized as a very promising direction in the paradigm of optimization. This paper presents a quantum entanglement inspired hard constraint handling approach, to efficiently solve discrete constrained optimization problems. We propose a quantum entanglement inspired representation method of hard constraints, which uses a series of qubits and quantum circuit to coherently represent the hard constraints in the posterior quantum states. A quantum entanglement inspired genetic algorithm (QEI-GA) is proposed by embedding the quantum entanglement inspired representation into classical genetic algorithm optimization strategy. The proposed method is applied to solve a real-world airport workforce shift planning problem to validate the algorithm’s effectiveness and robustness. The results of the case study show that the proposed method can consistently reach better solutions for the discrete constrained optimization problem, even with a much smaller population size, compared to the conventional genetic algorithms with penalty function based and multi-objective concept-based strategies.

Inversion of airborne transient electromagnetic data based on reference point lateral constraint

The airborne transient electromagnetic method (ATEM) has become a popular tool in mineral and resource exploration due to its speed and high efficiency. The distance between two adjacent measured points is minuscule for a flight line because of the oversampling in the ATEM, which results in a sharp change in the horizontal direction. The conventional lateral constraint inversion method accumulates errors and has a significant calculation cost. Therefore, a laterally constrained, segmented inversion method based on a reference point is proposed to reduce errors and improve accuracy. Electrical information about the known logging data is employed as the lateral constraint of the reference point; alternatively, the inversion results of previous point can be used as lateral constraints, which guarantees a continuous inversion profile. The golden-section method is applied to solve the Lagrange multiplier to ensure a quick and effective convergence of inversions. Constraint inversions of a one-dimensional model show that the strategy converges quickly, and the inversion results are closer to the true resistivity of the model. Constraint inversion of a 2D model indicates that lateral constraints considerably reduce the discontinuity of a single-point inversion and smooths the inversion's resistivity pseudo-section. Finally, the effectiveness of the proposed algorithm is verified by the inversion of the airborne transient electromagnetic survey data in Xinjiang province.

Refining characteristic constraint modes of component mode synthesis with residual modal flexibility

The Craig–Bampton component mode synthesis uses interface constraint modes and deformed substructural modes for model reduction. In some instances, it may be of interest to realize additional reductions after reducing the substructural degrees of freedom, and this is generally done by the reduction of the interface degrees of freedom. The characteristic constraint modes were developed to achieve the interface reduction of the Craig–Bampton method. However, the interface reduction process can compromise accuracy because using a small number of characteristic constraint modes, focusing on low-frequency range, may interrupt vibration energy transmission between substructures in relatively mid and/or high-frequency ranges. Based on this motivation, an improved reduction technique of the interface regions is introduced for refining characteristic constraint modes. Considering a residual modal effect is the main point of refinement of the characteristic constraint modes. The modified characteristic constraint modes and fixed-interface normal modes computed by a newly derived formulation with residual modal flexibility can represent the characteristics of interface motion better than the conventional constraint modes. Consequently, the improved transformation matrix may facilitate vibration energy transmission between substructures. Numerical experiments indicate that the proposed refinement method can complement the conventional method in terms of accuracy, and the refined constraint and fixed interface normal modes are better representations of the original mode shapes than conventional approaches are.

Seismic structure-constrained inversion of CSAMT data for detecting karst caves

Karst cave is a sort of special and buried geological structure that was widely developed in the Permo-Carboniferous coal accumulation area of North China. It brings karst collapse and safety hazard in the mining industry. In this study, we propose a seismic structure-constrained inversion of controlled source audio-frequency magnetotelluric (CSAMT) data on a detailed survey and detection of karst caves. Instead of constrained by seismic impedance, the method in this study directly takes the seismic imaging results as structural constraints, which is different from the cross-gradient technique used by conventional structural constraints. First, the seismic migration section is divided according to the CSAMT inversion grid and applied pixel extraction for each grid. Clustering is carried out according to the structural information interpreted by the seismic migration section and the average pixel value of each cluster is calculated. Then the clustered results were used in the seismic structure-constrained inversion of CSAMT data based on cross-gradient technique. After that, as a karst cave model developed in limestone was established, the study compares the structure-constrained inversions with different clustering strategies shows a much more precision of karst cave detection than the method only applies CSAMT data. Moreover, experimental verification is provided in this study, which is for the detection of a suspected karst collapse column from Shandong Province, China. The comparison results further show that the structure-constrained inversion method proposed in this paper is applicable and may effectively improve the locating accuracy of karst caves.

Robustifying Payload Carrying Operations for Quadrotors Under Time-Varying State Constraints and Uncertainty

Quadrotors find a vast potential use in delivery and disaster relief operations. Control becomes critical in such scenarios, especially when quadrotors have to manoeuvre through constrained spaces or deliver payloads at precise locations in the presence of external disturbances and parametric uncertainties stemming from uncertain payloads. Therefore, the controller has to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guarantee a predefined tracking accuracy not to violate the state constraints</i> . On the other hand, conventional fixed-valued state constraints are not suitable in many scenarios such as (i) initial offset being well beyond the expected accuracy, (ii) system dynamics experiencing significant transients due to the dropping of the payload. However, to the best of the authors’ knowledge, state-of-the-art controllers do not provide any solution for an underactuated system like a quadrotor when the system needs to honour time-varying constraints under uncertainties. This work proposes a controller for quadrotors which is robust against external disturbances and parametric variations and guarantees a time-varying predefined position, velocity, attitude, and attitude-rate accuracy. The closed-loop system stability is established analytically, and the effectiveness of the proposed controller is validated experimentally compared to the state-of-the-art under a precision payload delivery scenario.

Uniform Ray Description of Physical Optics Scattering by Finite Locally Periodic Metasurfaces

This article presents a uniform ray description of electromagnetic wave scattering by locally periodic metasurfaces of polygonal shape. The model is derived by asymptotically evaluating the critical-point contributions of a physical optics (PO) scattering integral. It is valid for metasurfaces whose bulk scattering coefficients are periodic functions of a phase parameter, which, in turn, is a continuous and smooth function of surface coordinates. The scattered field is expressed in terms of reflected, transmitted, and diffracted rays that do not generally obey conventional geometrical constraints (i.e., Snell’s law and the Keller cone). An iterative technique is presented to determine the locations of critical points on one or multiple interacting metasurfaces. Numerical results demonstrating the utility and accuracy of the asymptotic PO model are also provided.

MILP based Optimal Design of Hybrid Microgrid by Considering Statistical Wind Estimation and Demand Response

Recently due to technical, economical, and environmental reasons, penetration of renewable energy resources has increased in the power systems. On the other hand, the utilization of these resources in remote areas and capable regions as isolated microgrids has several advantages. In this paper, a hybrid microgrid, which includes photovoltaic (PV)/wind/energy storage, is investigated. It has been located in Iran-Khalkhal. The purposes of this study are optimal energy management and sizing of the microgrid. Since the magnitude of the harvested renewable energy deals severely and complexly with season and climate issues, planning of the system based on their specific values is an oversimplification. Therefore, in addition to conventional constraints such as environmental and operational ones, estimation of the wind speed at the site is considered. The Monte Carlo method is employed to model and estimate wind behavior. Also, for regulating production and demand in the microgrid the Demand Response (DR) program is conducted to improve the contribution of the renewable energy resources. The planning is constructed as an optimization problem. It is formulated as a Mixed Integer Linear Programming (MILP). By solving it, the size and production magnitude of energy sources, as well as storage conditions, are determined. Finally, the proposed method is simulated by GAMS for all seasons of two scenarios. The results show desirable energy management and cost reduction in the studied grid.

Real-time wavelet-based adaptive algorithm for low inertia AC microgrids power measurements

This paper proposes a real-time wavelet-based power estimator using a real-time modified stationary discrete wavelet packet transform (RT-MSDWPT). The proposed method overcomes the conventional SDWPT main constraints of power estimations related to the lack of reactive power flow direction and susceptibility to off-nominal frequency operation. The introduction of a digital sampling-shifting of a quarter of the fundamental, applied to the filtered voltages, assures the reactive power flow direction. Additionally, the insertion of an adaptive sampling time procedure based on the power grid frequency estimation mitigates the errors introduced by the network frequency deviations. These modifications make the RT-MSDWPT suitable for being embedded into the standard hierarchical power flow control strategies of ac microgrids. Experimental results assessed using an ac microgrid laboratory setup, operating under different operational scenarios, demonstrate the feasibility of the proposed solution.

Free-form optimization of nanophotonic devices: from classical methods to deep learning

Abstract Nanophotonic devices have enabled microscopic control of light with an unprecedented spatial resolution by employing subwavelength optical elements that can strongly interact with incident waves. However, to date, most nanophotonic devices have been designed based on fixed-shape optical elements, and a large portion of their design potential has remained unexplored. It is only recently that free-form design schemes have been spotlighted in nanophotonics, offering routes to make a break from conventional design constraints and utilize the full design potential. In this review, we systematically overview the nascent yet rapidly growing field of free-form nanophotonic device design. We attempt to define the term “free-form” in the context of photonic device design, and survey different strategies for free-form optimization of nanophotonic devices spanning from classical methods, adjoint-based methods, to contemporary machine-learning-based approaches.

Interface reduction in flexible multibody systems using the Floating Frame of Reference Formulation

The influence of interface reduction on the dynamics of flexible multibody systems has not been well investigated and the conventional multipoint constraints still suffer when applied to multibody systems. Here, we propose a novel approach to derive rigid and interpolation multipoint constraints exploiting the role of the reference conditions, used in the Floating Frame of Reference Formulation to cancel rigid body modes from the elastic field. Since the connection of the reference conditions to the multipoint constraints seems to be limited to the free-surface case, the proposed formulation combines the interface reduction problem to create a novel Component Mode Synthesis method able to work with any reference condition. Two case studies are presented to show the differences between models with different combinations of reference conditions and multipoint constraints.

Moiré-Enabled Topological Superconductivity.

The search for artificial topological superconductivity has been limited by the stringent conditions required for its emergence. As exemplified by the recent discoveries of various correlated electronic states in twisted van der Waals materials, moiré patterns can act as a powerful knob to create artificial electronic structures. Here, we demonstrate that a moiré pattern between a van der Waals superconductor and a monolayer ferromagnet creates a periodic potential modulation that enables the realization of a topological superconducting state that would not be accessible in the absence of the moiré. The magnetic moiré pattern gives rise to Yu–Shiba–Rusinov minibands and periodic modulation of the Majorana edge modes that we detect using low-temperature scanning tunneling microscopy (STM) and spectroscopy (STS). Moiré patterns and, more broadly, periodic potential modulations are powerful tools to overcome the conventional constraints for realizing and controlling topological superconductivity.

AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM

<h3>Abstract</h3> 3D lidar-based simultaneous localization and mapping (SLAM) is a well-recognized solution for mapping and localization applications. However, the typical 3D lidar sensor (e.g., Velodyne HDL-32E) only provides a very limited field of view vertically. As a result, the vertical accuracy of pose estimation suffers. This paper aims to alleviate this problem by detecting the absolute ground plane to constrain vertical pose estimation. Different from the conventional relative plane constraint, this paper employs the absolute plane distance to refine the position in the z-axis and the norm vector of the ground plane to constrain the attitude drift. Finally, relative positioning from lidar odometry, constraint from ground plane detection, and loop closure are integrated under a proposed factor graph-based 3D lidar SLAM framework (AGPC-SLAM). The effectiveness is verified using several data sets collected in Hong Kong.

Energy Efficiency Optimization of Reconfigurable Intelligent Surfaces With Electromagnetic Field Exposure Constraints

This work considers the problem of energy efficiency maximization in a RIS-based communication link, subject to not only the conventional maximum power constraints, but also additional constraints on the maximum exposure to electromagnetic radiations of the end-users. The RIS phase shifts, the transmit beamforming, the linear receive filter, and the transmit power are jointly optimized, and two provably convergent and low-complexity algorithms are developed. One algorithm can be applied to the general system setups, but does not guarantee global optimality. The second algorithm is provably optimal in a notable special case. The numerical results show that RIS-based communications can ensure high energy efficiency while fulfilling users' exposure constraints to radio frequency emissions.

Entry Guidance Command Generation for Hypersonic Glide Vehicles Under Threats and Multiple Constraints

A novel entry guidance command generation (EGCG) method for hypersonic glide vehicles (HGVs) is proposed in this paper. Apart from the conventional path constraints, terminal constraints, and multiple stationary geographic constraints, the method takes into account several threats that HGVs must avoid during the entry process. The threats are classified into covert threats and dynamic threats. The information of covert threats needs to be detected during the entry process, and the positions of dynamic threats are even unfixed. A piecewise analytical polynomial height-velocity profile is used to derive an analytical magnitude expression for bank angle commands. The profile is capable of taking full advantage of the width of the entry corridor and satisfying large range requirements in flight missions. An improved artificial potential field (IAPF) is introduced to formulate the lateral guidance law, which allows the HGV to pass all the waypoints, circumvent no-fly zones, and maneuver to avoid threats. Finally, several simulations are conducted to demonstrate the effectiveness of the designed method. The proposed EGCG method exhibits a superior ability to satisfy multiple constraints and avoid threats, accuracy to target point arrival, and strong robustness against uncertainties and deviations.

Multitrace Impedance Inversion Based on Structure-Oriented Regularization

As an effective approach of reservoir prediction, poststack impedance inversion has been widely used in industry. However, like other inverse problems, poststack impedance inversion is a quintessential ill-conditioned problem. Conventional impedance inversion methods often use regularization techniques such as Tikhonov-type regularization to improve the stability of the inversion solution. Nevertheless, because this type of regularization method constrains the inversion process by applying isotropic smoothness to the impedance, it will result in blurred boundaries and micro-geological structures, thereby reducing the resolution of the inversion result. In order to address this problem, we have introduced a structure-oriented regularization (SOR) method based on the local geological structural direction for impedance inversion. Compared with conventional isotropic smooth constraints, SOR can apply smoothness along the direction of geological structures, so it can effectively protect significant geological information from being blurred. Three steps are required to complete the SOR-based seismic impedance inversion method. To begin with, the structural orientations are estimated from seismic image. Then, the SOR term is constructed and combined with the multitrace seismic data misfit term to formulate the objective function for the inversion of impedance. Finally, the objective function can be easily solved by the conjugate gradient (CG) method. We compare our method with the classic model-based impedance inversion method on synthetic and real seismic data. The inversion results demonstrate the benefit of our method in seismic impedance inversion.

Per-voxel constraints to minimize hot spots in linear energy transfer-guided robust optimization for base of skull head and neck cancer patients in IMPT.

Due to the employment of quadratic programming using soft constraints to implement dose volume constraints and the "trial-and-error" procedure needed to achieve a clinically acceptable plan, conventional dose volume constraints (upper limit) are not adequately effective in controlling small and isolated hot spots in the dose/linear energy transfer (LET) distribution. Such hot spots can lead to adverse events. In order to mitigate the risk of brain necrosis, one of the most clinically significant adverse events in patients receiving intensity-modulated proton therapy (IMPT) for base of skull (BOS) cancer, we propose per-voxel constraints to minimize hot spots in LET-guided robust optimization. Ten BOS cancer patients treated with IMPT were carefully selected by meeting one of the following conditions: (1) diagnosis of brain necrosis during follow-up; and (2) considered high risk for brain necrosis by not meeting dose constraints to the brain. An optimizing structure (BrainOPT) and an evaluating structure (BrainROI) that both contained the aforementioned hot dose regions in the brain were generated for optimization and evaluation, respectively. Two plans were generated for every patient: one using conventional dose-only robust optimization, the other using LET-guided robust optimization. The impact of LET was integrated into the optimization via a term of extra biological dose (xBD). A novel optimization tool of per-voxel constraints to control small and isolated hot spots in either the dose, LET, or combined (dose/LET) distribution was developed and used to minimize dose/LET hot spots of the selected structures. Indices from dose-volume histogram (DVH) and xBD dose-volume histogram (xBDVH) were used in the plan evaluation. A newly developed tool of the dose-LET-volume histogram (DLVH) was also adopted to illustrate the underlying mechanism. Wilcoxon signed-rank test was used for statistical comparison of the DVH and xBDVH indices between the conventional dose-only and the LET-guided robustly optimized plans. Per-voxel constraints effectively and efficiently minimized dose hot spots in both dose-only and LET-guided robust optimization and LET hot spots in LET-guided robust optimization. Compared to the conventional dose-only robust optimization, the LET-guided robust optimization could generate plans with statistically lower xBD hot spots in BrainROI (VxBD,50Gy[RBE], p=0.009; VxBD,60Gy[RBE], p=0.025; xBD1cc, p=0.017; xBD2cc, p=0.022) with comparable dose coverage, dose hot spots in the target, and dose hot spots in BrainROI. DLVH analysis indicated that LET-guided robust optimization could either reduce LET at the same dose level or redistribute high LET from high dose regions to low dose regions. Per-voxel constraint is a powerful tool to minimize dose/LET hot spots in IMPT. The LET-guided robustly optimized plans outperformed the conventional dose-only robustly optimized plans in terms of xBD hot spots control.

Long-Haul Vehicle Routing and Scheduling with Biomathematical Fatigue Constraints

Hours of service (HOS) regulations are among the conventional safety constraints that are compiled by long-haul truck drivers. These regulations have been considered in models and algorithms of vehicle routing problems to assign safe schedules to drivers. However, the HOS regulations neglect a few crucial fatigue risk factors and, at times, fail to generate fatigue-reducing schedules. In this study, a set of biomathematical fatigue constraints (BFCs) derived from biomathematical models are considered for a long-haul vehicle routing and scheduling problem. A BFC scheduling algorithm and a BFC-HOS scheduling algorithm have been developed and then embedded within a tabu search heuristic to solve the combined vehicle routing and scheduling problem. All the solution methods have been tested on modified Solomon instances and a real-life instance, and the computational results confirm the advantages of employing a sophisticated and fatigue-reducing scheduling procedure when planning long-haul transportation.

Gaze prediction for first-person videos based on inverse non-negative sparse coding with determinant sparse measure

Gaze prediction is a significant approach for processing a large amount of incoming visual information of videos. Recent gaze prediction algorithms often employ sparse models with the assumption that every superpixel in the video frames can be represented as linear combinations of a few salient superpixels. However, they are not actuated enough because of the insufficient knowledge that video signals contain a non-negative request. Hence, we develop a novel gaze prediction based on an inverse sparse coding framework with a determinant sparse measure. By introducing this sparse measure, the solutions are non-negative and sparser than conventional sparse constraints. However, the proposed optimization problem becomes nonconvex, which is difficult to solve. To efficiently address the corresponding nonconvex optimization problem, we propose a novel algorithm based on the difference in convex function programming, which can yield the global solutions. Experimental results indicate the improved accuracy of the proposed approach compared with state-of-the-art algorithms.