Mode Selection Approach Research Articles

Mode selection and combination rules of energy-based multimodal pushover method for simply supported beam bridges

The energy-based method (EBM) has been improved as Energy-based Multimodal Pushover Method (EMPM), and is introduced and verified to be effective and convenient for evaluate the seismic performance of the simply supported beam bridges. This paper mainly focuses on the research of vibration mode selection and combination rules of EMPM. Firstly, the longitudinal vibration modes of simply supported beam bridge are categorized into five cases based on mode shapes, and the case definition is emphasized. Then, corresponding to each case, the Pushover loading force are established, and the modal energy, modal displacement, and modal energy capability curve are proposed. After that, the energy-based demand curve and the corresponding energy coefficient for each case is recommended. Finally, only the mode with maximum mode participation factor (MPF) of each case is suggested to take part in the calculation of the seismic response with the proposed mode participation factor combination rule. Case studies of a practice bridge under Chi-Chi and El Centro earthquake waves are used to illustrate the effectiveness and accuracy of the proposed mode selection and combination rules. The study found that the EMPM with the proposed rules could effectively and rapidly assessing the seismic performance of the simply supported beam bridge, resulting in a 70 % decrease in time consumption compared with the Nonlinear Time History Analysis Method (NTHAM). Compared with the CQC and SRSS combination results, the proposed Mode Participation Factor (MPF) combination rule reduced the maximum relative deviation from 39.4 % to 21.09 % between EMPM and NTHAM for peak displacement. Further research showed that the proposed mode selection approach only uses modes with the highest MPF in each case, decreased the number of modes involved more than 50 % and the result accuracy remained almost identical.

Cladding-pumped laser and amplifier for E- and S-bands based on multimode bismuth-doped graded-index fibers: toward "watt-level" output power.

In this Letter, we investigated the potential scalability of output power of a cladding-pumped laser and a power amplifier (booster) based on a multimode Bi-doped fiber (BDF) using the mode-selection approach. We fabricated the multimode double-clad graded-index (GRIN) fiber with a confined Bi-doped germanosilicate glass core with a diameter of ≈30 and ≈60 µm. Using femtosecond (fs) inscription technology with high spatial resolution, Bragg gratings of a special transverse structure allowing the selection of low-order modes were written into the core of BDFs. The operation features of the cladding-pumped multimode bismuth-doped GRIN fiber lasers with the inscribed Bragg gratings with various reflection coefficients were investigated. In addition, the behavior of the output power and the beam quality (M2 parameter) of the optical radiation of the developed devices was studied. The CW laser and booster operating at nearly 1.45 µm with maximum output powers of ≈0.8 and ≈1 W, respectively, based on the 60-µm-core BDF under pumping by multimode laser diodes at 808 nm were developed, which are, to the best of our knowledge, the most powerful cladding-pumped BDF devices to date. Near single-mode lasing (M2 <1.3) is demonstrated for a 30-µm-core fiber. The experimental data open new possibilities to achieve higher powers in cladding-pumped BDF sources, which are more cost-effective compared to core-pumped counterparts.

Dynamic mode selection and resource allocation approach for 5G-vehicle-to-everything (V2X) communication using asynchronous federated deep reinforcement learning method

5G vehicle-to-everything (V2X) connectivity is crucial to enable future complex vehicular networking environment for enabling intelligent transportation systems (ITS). But, for mission critical applications like safety applications, unreliable vehicle-to-vehicle (V2V) connections and heavy signaling overheads in centralized resource distribution methods are becoming key obstacles. This work discusses the popular optimization issue of the selection of transmission mode and the allocation of resources blocks for 5G-V2X communication scenario. The stated problem is conceived as a Markov decision-making mechanism, and a Decentralized Deep reinforcement Learning (DRL) algorithm is presented to optimize the aggregate potential in terms of channel capacity of vehicle-to-infrastructure users while fulfilling the latency and reliability constraints of V2V communication link sets. In addition, considering training limitation of local DRL models, a two-timed synchronous federated DRL algorithm is used for making system robust. Therefore, we use two-timescale asynchronous federated deep reinforcement learning algorithm, i.e. Large-scale model and small- scale model. In large-scale model a graph-centered vehicle clustering is done to form the cluster of neighboring vehicles on a large timescale, whereas in small timescale model the vehicles in the similar cluster are used to train using robust global asynchronous federated deep reinforcement learning algorithm. The effects of outage threshold and vehicular density with respect to the network performance is presented. The simulation findings have shown that the proposed work outperforms the other previous state of the art works. The overall preeminence and convergence of the proposed work is verified.

Generation of ultrashort vortex pulses by spiral array

The ultrashort orbital angular momentum pulses, or the ultrashort optical vortex pulses, with the properties of the ultrashort duration and broadband spectrum, open significant applications in various contexts. The controllability of the orbital angular momentum in ultrashort vortex pulses crucially affects its capabilities. We put forward an approach for orbital angular momentum mode selection in an ultrashort laser pulse. This approach depends on an optical mask with multiple spirals rotationally and symmetrically arranged in a model of the spiral array. Both theoretical analyses and experimental work in the context of ultrashort vortex pulses demonstrate that the spiral array enables flexible generation of the structured orbital angular momentum modes. This optical mask shows the advantages of damage resistance, without medium-dispersion, and ease of handling in an ultrafast optical field. Furthermore, the spiral array can be applied to analyzing and generating other matter vortex beams within an axially scalar system.

When UAV and ad-hoc NOMA-BS meets in Disaster: A scheduling and mode selection approach

When UAV and ad-hoc NOMA-BS meets in Disaster: A scheduling and mode selection approach

A novel acceleration-based mode selection approach in mild hybrid vehicles

When the regulations were first formulated in 2008, one of the potential way’s automakers were supposed to meet the goal of 95g CO2/km by 2020/21 was the hybridization of vehicles. Market participants and customers are becoming more interested in electric vehicles. Infrastructure, high-power device complexity, driving distance and requirements for protection all seem to be obstacles that will take longer than anticipated to integrate, followed by fuel cell vehicles, which will be the new significant thing, particularly in the commercial vehicle segment. As an outcome, hybrid vehicles with 48V architecture are perceived as the forerunners of complete electrification. It outperforms traditional in terms of fuel efficiency and high voltage hybrids in terms of price and time-to-market. Evaluating a control approach for torque management between the motor and engine and mode change decision, namely, from electric to the hybrid mode or likewise, is a major challenge for automotive service providers for hybrid electric cars. Each of these dynamics will directly affect fuel efficiency and manoeuvrability, notably during mode transition. Research work presented and examines a new idea for improved driving dynamics on a P3 mild gasoline car in Modified Indian Driving Cycle (MIDC) 1ST stage (urban) under system constraints such as sustaining 12V battery voltage, lower battery 48V SOC, and so on. The novel method that has been implemented is focused on two techniques. I. A modified Adaptive Equivalent fuel consumption minimization strategy for the engine’s and the motor’s power distribution, and II) A rules-based mode change decision.

A Bandit Approach for Mode Selection in Ambient Backscatter-Assisted Wireless-Powered Relaying

Backscattering assisted wireless-powered communication combines ultralow-power backscatter transmitters with energy harvesting devices. This paper investigates the transmission mode selection problem of a hybrid relay that forwards data by switching between the active wireless-powered transmission and the passive ambient backscattering. It first presents a hybrid relay system model and derives its end-to-end success probability under theoretically optimal, but practically unrealistic, conditions. The transmission mode selection is then formulated as a stochastic two-armed bandit problem in a varying environment where the distributions of rewards are nonstationary. The proposed model selection scheme does not assume to have access to any channel states or network conditions, but merely relies on learning from past transmission records. Numerical analyses are performed to validate the proposed bandit-based mode selection approach.

Energy-Efficient SWIPT-Empowered D2D Mode Selection

While mode selection has been envisioned as the most cost-effective way to address the interference issue in Device-to-Device (D2D) communications, existing works have been largely conducted without consideration of the energy depletion of devices. In this paper we investigate simultaneous wireless information and power transfer (SWIPT) empowered mode selection based on stochastic geometry. As a mean of solving it, system energy efficiency is formulated by determining the closed-form ergodic energy-harvested and ergodic capacity of D2D and cellular users in reuse, dedicated, and cellular communication modes with the time switching and power splitting architectures of SWIPT. We then leverage the derived results, along with the energy efficiency to design an energy-efficient mode selection mechanism. Our simulation results show that the developed mechanism is able to select the best mode for D2D communication with better energy efficiency, especially in an ultra-dense cellular network as compared with a state-of-the-art mode selection approach.

Distributed vs centralized game theory-based mode selection and power control for D2D communications

The exponential growth cellular networks traffic has driven academia and industry to consider new channel access methods in addition to the conventional infrastructure-based access via cellular nodes. Long-Term Evolution-Advanced (LTE-A) techniques are suggested by the 3rd generation partnership project (3GPP), in order to offer both higher spectrum efficiency and data rate. In fact, LTE-A has attracted a great attention in the cellular domain, since it improves the LTE by proposing a lot of variety in terms of new techniques such as carrier aggregation, multiple-input multiple-output (MIMO), device-to-device (D2D) communications, etc. Notably, D2D communications underlaid offer several advantages for the LTE-A, as wireless peer-to-peer services and higher spectral efficiency. In LTE-A, the power control and resource allocation algorithms can generally be categorized into two classes: centralized and distributed approaches. In fact, power control and resource allocation are performed by the base station in centralized approach and by the UEs in distributed one.In this investigation, we propose first a mode selection approach based on a predetermined SINR threshold, which is required by each kind of user: cellular and D2D users. The amount of minimum and maximum power are then derived to fulfill the predetermined requirements, by limiting the interference created by underlaid D2D users. Unlike the previous works, this paper provides a mathematical demonstration to achieve the minimum amount of power (Pareto power), which satisfies the requirements imposed by both users. Second, a centralized power control approach, which aims to generalize a classical approach for more than one cellular user is proposed. Third, a distributed power control approach is modeled as a non-cooperative pure game between the different types of users in order to maximize the two cellular and D2D utility functions. Finally, these proposed centralized and distributed power control approaches are compared with a classical centralized one, in terms of coverage probabilities for both cellular and D2D users.

Deep Reinforcement Learning-Based Mode Selection and Resource Management for Green Fog Radio Access Networks

Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

Energy- and Spectral-Efficient Adaptive Forwarding Strategy for Multi-Hop Device-to-Device Communications Overlaying Cellular Networks

Device-to-device (D2D) communications in cellular networks enable direct transmissions between user equipments (UEs). If the source UE (SUE) and the destination UE (DUE) are far away from each other or the channel between them is too weak for direct transmission, then multi-hop D2D communications, where relay UEs (RUEs) forward the SUE’s data packets to the DUE, can be used. In this paper, we propose an energy-efficient optimal adaptive forwarding strategy (OAFS) for multi-hop D2D communications. OAFS adaptively chooses between the best relay forwarding (BRF) mode and the cooperative relay beamforming (CRB) mode with the optimal number of RUEs, depending on which of them provides the higher energy efficiency (EE). To reduce the computational complexity for selecting the optimal RUEs for CRB mode, we propose a low-complexity sub-optimal adaptive forwarding strategy (SAFS) that selects between the BRF and the CRB with two RUEs by comparing their EE. Furthermore, a distributed forwarding mode selection approach is proposed to reduce the overhead for forwarding mode selection. The analytical and simulation results show that OAFS and SAFS exhibit significantly higher EE and spectral efficiency than BRF, CRB, direct D2D communications, and conventional cellular communications. SAFS is almost as energy- and spectral-efficient as OAFS.

Tradeoff Caching Strategy of the Outage Probability and Fronthaul Usage in a Cloud-RAN

In this paper, a tradeoff content caching strategy is proposed to jointly minimize the cell average outage probability and fronthaul usage in a cloud radio access network. At first, an accurate closed-form expression of the outage probability conditioned on the user's location is presented, and the cell average outage probability is obtained through the composite Simpson's integration. The caching strategy for jointly optimizing the cell average outage probability and fronthaul usage is then formulated as a weighted sum minimization problem, which is a nonlinear 0–1 integer problem. Two heuristic algorithms are proposed to solve the problem. First, a genetic algorithm (GA)-based approach is proposed. Numerical results show that the performance of the proposed GA-based approach with significantly reduced computational complexity is close to the optimal performance achieved by an exhaustive-search-based caching strategy, and the GA-based approach can improve the performance by up to 47.5% on average than the typical probabilistic caching strategy. Second, in order to further reduce the computational complexity, a mode selection approach is proposed. Numerical results show that this approach can achieve near-optimal performance over a wide range of weighting factors through a single computation.

A Distributed Mode Selection Approach Based on Evolutionary Game for Device-to-Device Communications

As one of the key technologies for the fifth generation (5G) mobile networks, device-to-device (D2D) communications offer promising benefits such as high spectrum efficiency, traffic offloading, and enhanced coverage. Depending on the resource sharing between D2D users and regular cellular users, a D2D user equipment (UE) can dynamically switch its communication mode to improve the quality of service (QoS) and user experience. Mode selection is an essential issue to ensure the QoS of D2D UEs while maximizing the system capacity. In this paper, we investigate the D2D mode selection problem from a novel perspective. In addition to the classic cellular mode and direct reuse mode, we further consider a relay mode for D2D UEs. Moreover, in order to address a potentially large population of D2D UEs, we propose an evolutionary game-based approach for D2D mode selection. The evolutionary game is formulated with a utility function that takes into account both the achievable throughput of D2D UEs and the radio resource consumption. Based on the evolutionary game formulation, we implement selection dynamics, i.e., replication by imitation, in a device-controlled mode selection algorithm. To evaluate the performance of the proposed mode selection algorithm, we conduct simulations to compare it with three baseline schemes, including an approach based on maximum signal-to-interference-plus-noise, a distance-based approach, and a random approach. As shown in the simulation results, the proposed approach achieves higher utilities than the baseline schemes.

RF Behavior and Launcher Design for a Fast Frequency Step-tunable 236 GHz Gyrotron for DEMO

AbstractAs part of the EUROfusion project, the conceptual design of a 1 MW 236 GHz hollow-cavity gyrotron is ongoing at IHM, KIT for a DEMOnstration Power Plant (DEMO), along with a 2 MW coaxial-cavity design concept. Fast frequency-tunable gyrotrons (tuning within a few seconds) are recommended for plasma stabilization using a non-steerable antenna. In this work, the mode-selection approach for such a frequency-tunable gyrotron is presented and suitable operating modes for fast frequency tunability are suggested. Magnetic field tuning has been studied as an effective technique to tune the gyrotron operating frequency. The step-tunability of the 236 GHz gyrotron within the frequency range of ±10 GHz in steps of 2–3 GHz is demonstrated in numerical simulations. A hybrid-type Quasi-Optical Launcher (QOL) has been designed for a step-frequency tunable gyrotron with sufficiently high Fundamental Gaussian Mode Content (FGMC).

A combined spectral-amplitude-perturbation approach for systematic mode selection in thermal convection

Purpose– The post-critical convective state for Rayleigh-Benard (RB) convection is studied using a nonlinear spectral-amplitude-perturbation approach in a fluid layer heated from below. The paper aims to discuss these issues.Design/methodology/approach– In the spectral method the flow and temperature fields are expanded periodically along the layer and orthonormal shape functions are used in the transverse direction. A combined amplitude-perturbation approach is developed to solve the nonlinear spectral system in the post-critical range, even far from the linear stability threshold. Also, to leading order, the Lorenz model is recovered.Findings– It is found that very small Prandtl numbers (Pr &lt; 0.1) can change the Nusselt number, when terms to O(ε5/2) and higher are considered. However, to lower orders the Prandtl number does not affect the results. Variation of the Nusselt number to different orders is found to be highly consistent. Comparison with experimental results is made and a very good qualitative agreement is observed, even far from the linear threshold.Originality/value– Unlike existing nonlinear formulations for RB thermal convection, the present combined spectral-perturbation approach provides a systematic method for mode selection. The number and type of modes to be included are directly related to the post-critical Rayleigh number. The method is not limited to the weakly nonlinear range.

Mode selective generation of guided waves by systematic optimization of the interfacial shear stress profile

Piezoelectric transducers are commonly used in structural health monitoring systems to generate and measure ultrasonic guided waves (GWs) by applying interfacial shear and normal stresses to the host structure. In most cases, in order to perform damage detection, advanced signal processing techniques are required, since a minimum of two dispersive modes are propagating in the host structure. In this paper, a systematic approach for mode selection is proposed by optimizing the interfacial shear stress profile applied to the host structure, representing the first step of a global optimization of selective mode actuator design. This approach has the potential of reducing the complexity of signal processing tools as the number of propagating modes could be reduced. Using the superposition principle, an analytical method is first developed for GWs excitation by a finite number of uniform segments, each contributing with a given elementary shear stress profile. Based on this, cost functions are defined in order to minimize the undesired modes and amplify the selected mode and the optimization problem is solved with a parallel genetic algorithm optimization framework. Advantages of this method over more conventional transducers tuning approaches are that (1) the shear stress can be explicitly optimized to both excite one mode and suppress other undesired modes, (2) the size of the excitation area is not constrained and mode-selective excitation is still possible even if excitation width is smaller than all excited wavelengths, and (3) the selectivity is increased and the bandwidth extended. The complexity of the optimal shear stress profile obtained is shown considering two cost functions with various optimal excitation widths and number of segments. Results illustrate that the desired mode (A0 or S0) can be excited dominantly over other modes up to a wave power ratio of 1010 using an optimal shear stress profile.

A multi-reference-based mode selection approach for the implementation of NExT-ERA in modal-based damage detection

A modal-based global method for identification, localization and quantification of damage of a shear frame structure is presented in this paper. The method involves identification of modal parameters from the vibration responses of the structure only (output only) using a combination of natural excitation technique (NExT) and eigensystem realization algorithm (ERA), and determination of stiffness parameters using very few identified modes by employing a least squares solution procedure. Finally, damage is indicated by comparing the stiffnesses of the (possibly) damaged structure with that of the undamaged structure. The implementation of NExT-ERA requires a reference channel for modal identification, but several limitations of using a single reference channel have been identified from the investigations of this study. To overcome the limitations, a new approach called multi-reference-based mode selection is proposed in this paper. The approach makes use of all the available DOFs, but one at a time, as reference channels and identifies the best representative modes of the structure by a mode screening process and a mode separation process. The approach is also automated for expediency and possible real-time application. The presented damage detection methodology is implemented on the analytical phase I and experimental phase II of the IASC-ASCE SHM benchmark problem and shows an accuracy of around 90% in damage detection. Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd.

Combined Spectral-Perturbation Approach for Systematic Mode Selection in Thermal Convection

A nonlinear spectral approach is proposed to simulate the post critical convective state for thermogravitational instability in a Newtonian fluid layer heated from below. The spectral methodology consists of expanding the flow and temperature fields periodically along the layer, and using orthonormal shape functions in the transverse direction. The Galerkin projection is then implemented to generate the equations for the expansion coefficients. Since most of the interesting bifurcation picture is close to criticality, a perturbation approach is developed to solve the nonlinear spectral system in the weakly post critical range. To leading order, the Lorenz model is recovered. The problem is also solved using amplitude equations for comparison. The similarity and difference among the three models are emphasized.

A fuzzy hierarchy integral analytic expert decision process in evaluating foreign investment entry mode selection for Taiwanese bio-tech firms

The purpose of this study is to help bio-tech firms solve the foreign investment (FI) entry mode selection problem. This study combines the concepts of factor analysis, analytic hierarchy process (AHP), genetic algorithm (GA), and fuzzy integral to construct an entry mode selection approach. This study produces several interesting findings. (1) In the different investment entry modes, there are large differences in evaluation focus when investors select their entry modes. (2) For example, Taiwanese bio-tech firms entering mainland China consider merger and acquisitions to be the first priority, and followed by strategic alliances. This research shows that if the stock share holding is unlimited, Taiwanese bio-tech firms prefer to select a high stock share holding investing mode. (3) In various investment modes, the aspects of “Capital and Risk” and “Technology Ability” have the most consistent effect on entry mode selection.

Computationally efficient mode selection in H.264/AVC video coding

In the H.264/AVC video coding standard, the mode selection part requires a considerable amount of computation and memory access. This paper presents a fast or computationally efficient mode prediction and selection approach which has the following attributes: (a) both the spatial and temporal information are used to achieve early termination using adaptive thresholds, (b) inclusion of a modulator capable of trading off computational efficiency and accuracy and (c) a homogenous region detection procedure for 8times8 blocks based on adaptive thresholds. It is shown that the above attributes lead to a computationally efficient video coding implementation as compared to the previous fast approaches. The results obtained on QCIF, CIF, and HD format video sequences based on two different implementation platforms JM12 and x264 are presented to show the computational efficiency of the developed approach at the expense of acceptably low losses in video quality. Improvement in memory access performance is also demonstrated using a DSP processor simulator.