Low-level Object Research Articles

5G Multi-Slices Bi-Level Resource Allocation by Reinforcement Learning

As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical industries makes its resource allocation also have an obvious hierarchical structure. In this paper, we propose a bi-level resource allocation model. The up-level objective in this model refers to the profit of the 5G operator through the base station allocating resources to slices. The lower-level objective in this model refers to the slices allocating the resource to its users fairly. The resource allocation problem is a complex optimization problem with mixed-discrete variables, so whether a resource allocation algorithm can quickly and accurately give the resource allocation scheme is the key to its practical application. According to the characteristics of the problem, we select the multi-agent twin delayed deep deterministic policy gradient (MATD3) to solve the upper slice resource allocation and the discrete and continuous twin delayed deep deterministic policy gradient (DCTD3) to solve the lower user resource allocation. It is crucial to accurately characterize the state, environment, and reward of reinforcement learning for solving practical problems. Thus, we provide an effective definition of the environment, state, action, and reward of MATD3 and DCTD3 for solving the bi-level resource allocation problem. We conduct some simulation experiments and compare it with the multi-agent deep deterministic policy gradient (MADDPG) algorithm and nested bi-level evolutionary algorithm (NBLEA). The experimental results show that the proposed algorithm can quickly provide a better resource allocation scheme.

A unit commitment based-co-optimization of generation and transmission expansion planning to mitigate market power

• A unit-commitment-based generation and transmission expansion planning model is proposed. • The planning problem is subject to the market power's constraint. • The scenario-based model is structured as a mixed-integer bi-level programming (MIBP). • The lower-level problem computes the must-run generation (MRG) as a market index. • An exact algorithm using the reformulation-and-decomposition technique is utilized to attain the optimal solution. This paper aims to construct a novel strategy for coordinated generation and transmission expansion planning (CGTEP) considering market power. The integrated model constitutes bi-level programming from a centralized planning point of view. The upper level seeks to minimize the investment and operation cost subject to technical constraints, while the lower level's objective is to mitigate the market power. A stochastic unit commitment-based approach is employed at the upper level to find the least-cost solution and at the lower level to minimize the transmission-constrained market power index. Since the lower level contains integer variables, it is impossible to utilize the Karush-Kuhn-Tucker (KKT) conditions or duality theory (DT) to convert the bi-level problem into a single level. Hence, an exact algorithm using the reformulation-and-decomposition technique (RDT) is utilized to attain the optimal solution to this complex mixed-integer bi-level programming (MIBP) problem. The IEEE 118-bus electric power network is used for numerical studies. Results from the numerical studies signify the effectiveness of the presented model showing that a well-designated power system can significantly mitigate market power.

A Scheme to Improve Network Performance Based on Traffic Restriction and Pricing in the Presence of Carpooling

For the purpose of optimizing the traffic network in the presence of carpooling, a traffic demand management scheme was proposed with the consideration of traffic restriction, carpooling, and road pricing. We establish a variational inequality describing the travelers’ mode and route choice following the logit mode split and multimodal user equilibrium principle in the condition of elastic demand. A bilevel programming was designed with the variational inequality as the lower level and optimization objective function taking into account traffic congestion and social welfare as the upper level. The results show that this scheme is possible to be effective in improving network performance. The higher average occupancy of carpooling vehicles and the carpooling demand may not bring better network performance due to the topology, parameters of the network, and travel demand. The fare of carpooling travelers is possible to impact the network very slightly with this scheme. Besides, the group of travelers who is able to afford more than one vehicle is hardly affected by this scheme.

Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks

Visible light communication (VLC) provides an alternative underwater wireless connectivity solution with its low latency and high data rates albeit at relatively shorter distances in the order of tens of meters. In the context of underwater sensor networks (USNs), VLC is particularly suitable to establish connectivity between “data mule” autonomous underwater vehicles (AUVs) and sensor nodes since communications is enabled only when the sensor node and mule AUV are in close proximity. In this paper, we consider a USN scenario where a solar-powered AUV gathers data from the sensor nodes using VLC signaling. We formulate a three-dimensional trajectory optimization for solar-powered AUVs with the goal of maximizing the harvested energy under constraints imposed by the data transmission. The optimization constraints include the minimum required data transfer rate, therefore a corresponding transmission distance, between the sensors and the AUV. We formulate the problem as a bilevel optimization problem. The lower-level objective function is in the form of traveling salesman problem which determines the optimum sequence order of the sensor nodes to be visited while the upper-level objective function is the optimization of the trajectory between each pair of adjacent nodes for the given order of node visits. Our numerical results demonstrate that the proposed trajectory significantly prolongs the mission time and autonomous operation of the AUV without the need to return to home base. Furthermore, since the proposed trajectory optimization is reactive to ocean currents, it brings reductions in the energy consumption of the AUVs.

Extension of the value function reformulation to multiobjective bilevel optimization

We consider a multiobjective bilevel optimization problem with vector-valued upper- and lower-level objective functions. Such problems have attracted a lot of interest in recent years. However, so far, scalarization has appeared to be the main approach used to deal with the lower-level problem. Here, we utilize the concept of frontier map that extends the notion of optimal value function to our parametric multiobjective lower-level problem. Based on this, we build a tractable constraint qualification that we use to derive necessary optimality conditions for the problem. Subsequently, we show that our resulting necessary optimality conditions represent a natural extension from standard optimistic bilevel programs with scalar objective functions.

Interval-parameter bi-level programming for energy system management under uncertainty: Towards a deep-decarbonized and sustainable future in China

To reach the goal of carbon-neutral society, synergistic approaches based on mathematical models that are capable of planning energy system associated with complex uncertainty and hierarchical structure are desired. In this study, an interval-parameter bi-level programming (IBP) method that is effective for solving hierarchical decision-making problems with conflict objectives and uncertainty is developed. Then, an IBP based China's energy system model (abbreviated as IBP_CES) is formulated for mainland China during 2021–2050, where the higher-level objective of minimizing carbon dioxide (CO2) emission is given priority, following by the lower-level objective of minimizing system cost. Results from IBP_CES show that, although China's energy supply would still rely heavily on fossil fuels, the share of coal would decrease to 33.9% by 2050. Results also disclose that the national CO2 emission would reach the peak value during 2026-2030 and then decline rapidly due to development of renewable energy and promotion of carbon-treatment technology which is beneficial for government to achieve carbon neutrality goals. In addition, compared to the traditional single-level model, CO2 emission from IBP_CES can decrease by 5.9%, demonstrating that IBP_CES is more superior in energy system decarbonization. The findings can help decision makers to well understand the interactions and tradeoffs between climate mitigation and economic development, and provide effective information on clean, sustainable and deep-decarbonized development strategies.

A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective.

Thermal-hydraulic analysis of superconducting cables for energy applications with a novel open object-oriented software: OPENSC2

Super-conducting cables are an enabling technology for energy applications such as large magnetic-confinement nuclear fusion machine, and a promising key player in the power transmission of the next future, both in AC and DC conditions. While the thermal–hydraulic analysis of forced-flow superconducting cables for fusion application can only rely on commercial or proprietary numerical tools, such kind of tools for power transmission cables are not even available. Within the framework of Open Science, set as a priority by the European Commission in Horizon Europe, the novel software OPEN Super Conducting Cables (OPENSC2) has been developed to grant the entire research community the possibility to simulate thermal–hydraulic transients in forced-flow superconducting cables for energy applications. A Test-Driven Development has been adopted for the OPENSC2 within an object-oriented approach. Following the TDD approach, three test cases are considered of paramount interest for the OPENSC2 development, deriving the set of characteristics that the target object-oriented tool should comply with, and namely: 1) a heat slug propagation along an ITER-like 2-region cable-in-conduit conductor, with a thousand of mm-size low-critical-temperature superconducting (LTS) strands, cooled by supercritical helium (SHe); 2) the heat diffusion across the cross section of a twisted-slotted-core cable-in-conduit conductor, with high-critical-temperature (HTS) superconducting tapes, for fusion application, cooled by SHe and 3) the nominal operation of a single-phase HTS High-voltage, Direct Current power cable, with a 2-cryostat configuration and 2 different fluids adopted as primary coolant and thermal shield. In the object-oriented OPENSC2 the class “conductor” is defined, where each Conductor Object (CO) is the combination of different lower-level objects (both fluid and solid components) instantiated by the class. The choice of each component drives the automatic selection of the appropriate physical equation(s) in the code, as well as the possible interactions between them. Thermo-physical properties of different materials and cryogens can be attributed to the components of a conductor objects, taken form open datasets. A user-friendly GUI allows setting and monitoring the simulations while running. The software is tested in the three case studies targeted in the TDD, to show eventually how it allows modeling the three test cases presented here. The Verification and Validation of the CO methods performed through benchmarks against the 4C code is also presented and discussed.

Attention can operate on object representations in visual sensory memory.

Numerous studies have shown that attention can be allocated to various types of objects, such as low-level objects developed by perceptual organization and high-level objects developed by semantic associations. However, little is known about whether attention can also be affected solely by object representations in the brain, after the disappearance of physical objects. Here, we used a modified double-rectangle paradigm to investigate how attention is affected by object representation in visual sensory memory when the physical objects disappear for a short period of time before the target onset. By manipulating the interstimulus interval (ISI) between the offset of the objects and the onset of the target, an object-based attention effect, with shorter reaction times (RTs) for within-object relative to between-object conditions, was observed in the short-ISI (within 500 ms in Experiments 1a, 1b, 2, and 3) conditions while disappearing in the long-ISI (800 ms in Experiment 4) conditions. This result demonstrated that the mere presence of object representation in visual sensory memory, or the sensory memory-maintained object, can serve as an object unit that attention can operate on. This provides evidence for the relationship between object-based attention and visual sensory memory: object representation in visual sensory memory could affect attentional allocation, or attention can operate on a sensory memory-maintained object.

A dual-randomness bi-level interval multi-objective programming model for regional water resources management

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions. It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 108 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

On continuity in risk-averse bilevel stochastic linear programming with random lower level objective function

We study bilevel stochastic linear programs with fixed lower level feasible set and random follower's goal function and show that the parametrized random variable arising as the upper level outcome depends continuously on the leader's decision w.r.t. any Lp-norm with p∈[1,∞). This entails continuity of the objective function for a class of models involving convex risk measures defined on appropriate Lp-spaces and allows to formulate verifiable sufficient conditions for the existence of optimal solutions.

Comparative analysis of image projection-based descriptors in Siamese neural networks

Low-level object matching can be done using projection signatures. In case of a large number of projections, the matching algorithm has to deal with less significant slices. A trivial approach would be to do statistical analysis or apply machine learning to determine the significant features. To take adjacent values of the projection matrices into account, a convolutional neural network should be used. To compare two matrices, a Siamese-structure of convolutional heads can be applied. In this paper, an experiment is designed and implemented to analyze the object matching performance of Siamese Convolutional Neural Networks based on multi-directional image projection data. A backtracking search-based Neural Architecture Generation method is used to create convolutional architectures, and a Master/Worker structured distributed processing with highly efficient scheduling based on the Longest Processing Times-heuristics is used for parallel training and evaluation of the models. Results show that the projection-based methods are Pareto optimal in terms of one-shot classification accuracy and memory consumption.

Cognitive levels in testing knowledge in evidence-based medicine: a cross sectional study

BackgroundKnowledge assessment in evidence-based medicine (EBM) is usually performed by the measurement of memorised facts, understanding of EBM concepts and application of learned knowledge in familiar situations, all of which are considered lower-level educational objectives. The aim of this study was to assess EBM knowledge both on higher and lower cognitive levels across EBM topics.MethodsIn order to assess knowledge on different EBM topics across learning levels, we created a knowledge test (Six Progressive Levels in Testing – SPLIT instrument), which consists of 36 multiple choice items and measures knowledge in EBM at six cognitive levels (Remembering, Understanding, Applying, Analysing, Evaluating and Creating) and addresses six EBM topics (Evidence-based practice, Internal validity, Clinical importance, Study design, Sources of evidence, Diagnostic studies). Three independent assessors defined the minimum passing score (MPS) for the overall test, based on the first-year course content and educational objectives. The instrument was assessed in a sample of first- (n = 119) and third-year medical students (n = 70) and EBM experts (n = 14).ResultsThe MPS was 16 correct answers out of total 36 questions, and was achieved by 21 out of 119 first-year students, 14 out of 70 third-year students and 9 out of 14 EBM experts (χ2 = 13.3; P < 0.001, with significantly higher proportion of experts passing compared to students). Although experts had the highest scores overall, none of the groups outperformed others on individual cognitive levels, but the experts outperformed students in EBM topics of Study design and Sources of evidence (P = 0.002 and 0.004, respectively, Kruskal-Wallis test). First- and third-year students performed better on specific course topics taught in that study year (Diagnostic studies and Clinical relevance, respectively).ConclusionEBM knowledge of students and experts differ according to the specificities of their education/expertise, but neither group had excellent knowledge in all areas. It may be difficult to develop a knowledge test that includes different EBM topics at different cognitive levels to follow the development of specific and general aspects of EBM knowledge.

Some remarks on R. Carnap’s concept of construction in Aufbau and E. Hessserl’s concept of constitution

This paper turns to the concept of constitution and some to it more immediately related notions contained in R. Carnap's Aufbau and Husserl's works that might have had some influence there, in order to find similarities and differences between them. It hopefully will contribute to this assessment of the plausible hypothesis, recently raised in the literature, of an influence of the father of phenomenology not explicitly sufficiently credited in Aufbau. The argumentative strategy was to expose, firstly in a separate way, the relevant points in each strand and then to make the comparative examination. This first analysis took place by two general aspects, namely, to address, on the one hand, the very relation of constitution and what is a system of it and, given the implicated foundationalism attributed to such notion, the data which are at the base of system like this. Then I move on to examine the differences and similarities between such expositions. As result, similarities were observed in that the two of them imply a broader thesis of foundationalism; they propose, in this, to an encompassing system. In addition, they contend that the fact that there is a difference between a lower-level object and a higher-level object does not imply between them mereological composition or some necessary commonality of properties. However, there are also differences regarding the kind of relationship that the notions of constituting imply in each case. Distinctions were also found about the role of sensations in each system; but in that sense, there is, in both perspectives, also similarities concerning their status. The paper concludes with the notion that there must be husserlian influences in the Aufbau not sufficiently credited. However, it is unreasonable the diagnosis of extreme importance of Husserl's influence.

Simple bilevel programming and extensions

In this paper we discuss the simple bilevel programming problem (SBP) and its extension, the simple mathematical programming problem under equilibrium constraints (SMPEC). Here we first define both these problems and study their interrelations. Next we study the various types of necessary and sufficient optimality conditions for the (SMPEC) problems, which occur under various reformulations. The optimality conditions for (SBP) are special cases of the results obtained for (SMPEC) when the lower level objective is the gradient of a convex function. Among the various optimality conditions presented in this article are the sequential optimality conditions, which do not need any constraint qualification. We also present a schematic algorithm for (SMPEC), where the sequential optimality conditions play a key role in the convergence analysis.

Optimal sizing of user-side energy storage considering demand management and scheduling cycle

Battery energy storage systems (BESSs) can play a key role in obtaining flexible power control and operation. Ensuring the profitability of the energy storage is the prerequisite to realize its reasonable applications in the power system. This paper establishes a bi-level optimal sizing of energy storage participating in demand management and energy arbitrage for industrial users. The BESS scheduling cycle and lifetime are considered in the optimization model. The proposed bi-level model is derived from a life-cycle economic analysis of energy storage based on the maximization of net profit over the entire life-cycle and profit over the scheduling cycle as upper- and lower-level objective functions, respectively. The Karush–Kuhn–Tucher (KKT) conditions are combined with a mixed-integer linear programming (MILP) approach to solve the optimization model. Case studies based on realistic industrial load data are used to validate the usefulness of the proposed method, with the simulation results confirming that the method can effectively improve the benefits of the energy storage system. Finally, the effect of the load characteristics and electricity price policies on the model results is analyzed.

Dual-Polarization and Low-Sidelobe Corrugated Rectangular Horn Antennas for Outdoor RCS Measurement

AbstractOutdoor radar cross section (RCS) measurement suffers background clutter. Such clutter needs to be minimized, especially when measuring low RCS level objects. To do this, a pair of corrugated horn antennas with a rectangular aperture has been designed, fabricated, and measured to improve the performance of outdoor RCS measurement. This antenna consists of a coaxial-waveguide adaptor, a square-rectangular waveguide transition, a corrugated rectangular horn, and chokes. A comparison of the performances of several corrugation profiles is provided. The tangential profile surpasses others because it provides the necessary low sidelobe level and narrow elevation beamwidth. The proposed antenna achieves −30 dB sidelobe level, fan-shaped beam pattern, and dual-polarization with −26 dB cross-polarization level over the band from 8.5 GHz to 12.5 GHz. The effectiveness of the antenna has been verified experimentally on a roof with two corner reflectors that were deployed intentionally to enlarge the effects of background clutter.

Research on Energy Storage and High Proportion of Renewable Energy Planning Considering Demand

Based on the high proportion of renewable energy connected to the active distribution network, this article studies the joint planning of demand-side response and energy storage. Firstly, a two-level optimization model is established for the planning of active distribution network. The upper level objective function is the investment, operation and maintenance cost of energy storage and fan, and the lower level objective function is the annual network loss cost. Then, the improved Longhorn algorithm is used to solve the two-layer programming model, and the combination of Longhorn beard algorithm and particle swarm optimization algorithm not only improves the iteration speed, but also improves the global searching ability. Finally, the example analysis proves the effectiveness of joint planning to solve the high proportion of renewable energy, thus improving the economic benefits.

A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response

In planning for a large-scale disaster, potential relief centers to accommodate evacuees need to be identified. The quantities of emergency commodities are prepared and stocked at relief centers in advance for possible disasters. In the event of a disaster, due to the different disaster severities and uncertain environment, some relief centers inevitably have surplus commodities, whereas some relief centers are still unmet. To use any surplus commodities effectively, a multi-commodity rebalancing process is necessary to rebalance the commodities among relief centers. However, various uncertainties make the multi-commodity rebalancing process extremely challenging, including uncertain demand and transportation-network availability. By recognizing those practical uncertainties, a bi-level stochastic mixed-integer nonlinear programming model is proposed to formulate this multi-commodity rebalancing problem. The upper-level objective is to minimize the total dissatisfaction level, which is measured by the expected total weighted unsatisfied demand, and the lower-level objective is to minimize the expected total transportation time. Finally, a case study on the Great Sichuan Earthquake in China is implemented; their results show that the proposed model facilitates effective decision-making in the practice of multi-commodity rebalancing.

Low-level object properties impact memory reconsolidation

Low-level object properties impact memory reconsolidation