Aggregation Problem Research Articles

Tikhonov regularization as a nonparametric method for uncertainty quantification in aggregate data problems

Dynamical systems are widely used in mathematical models in engineering and in the applied sciences. However, the parameters of these systems are usually unknown and they must be estimated from measured data. Performing parameter estimation and quantifying the uncertainty in these estimates becomes critical to realize the predictive power of dynamical systems. In some applications, only measurements about the states of an ensemble of systems are available, where each one evolves according to the same model but for different parameter values, leading to aggregate data problems. In this case, an approach proposed in the literature is to estimate a density that is consistent with some estimates of the expected value of some quantities of interest. To solve the problem in practice, the density is discretized and, to solve the resulting ill-posed problem, Tikhonov regularization is used. In this work, we propose a Bayesian model that shows this approach can be interpreted as a maximum a posteriori estimate for the density. We show that the infinite-dimensional problem defining the MAP can be reformulated as a finite-dimensional problem that does not require discretizing the density. In several cases of interest, this problem is convex, unconstrained, and the objective function is smooth. Thus, it can be solved using algorithms with optimal convergence rates. The trade-off is that the objective is defined by an integral. However, our results characterize the regularity of the integrand, allowing the use of tailored numerical schemes to approximate it. Furthermore, our theoretical results characterize the form of the optimal density, whereas our numerical results illustrate the performance of our method and confirm our theoretical findings.

Towards Promoting the Efficiency of Enterprise Investment Management with Artificial Intelligence

With the continuous development and optimization of artificial intelligence, enterprises have begun to pay attention to the use of artificial intelligence for the optimization of work and the improvement of efficiency, and in the management of enterprise investment, a number of companies have begun to use a large number of artificial intelligence technologies in the integration and analysis of data, information processing and analysis, risk management, efficiency improvement, and so on. Enterprises encounter problems in analyzing data and information in investment management, and problems in risk management, all of which affect the improvement of their investment returns. Enterprises face a huge amount of data in the process of investment management, it is difficult to calculate a large number of data models in a relatively short period of time, and it is impossible to analyze and extract more information. In the face of investment risk, the control of risk in the past investment process is more based on experience to judge, there is a great deal of subjective assumptions in risk avoidance. And in the investment process companies also want to increase returns, improve efficiency, avoid risk, and achieve a more automated and intelligent trading process. Enterprises need to use artificial intelligence to technically solve the problems of data aggregation, arithmetic, and analysis; use artificial intelligence to optimize the problem of processing large amounts of information; use artificial intelligence technology to enhance risk management and predictive analysis, strengthen intelligent decision-making and portfolio optimization, and provide insights into big data analysis and market trends. The wide application of artificial intelligence technology in enterprises also has a certain impact on the development of enterprises, and it has a certain impact on the planning, feasibility, whole-process control, investment evaluation, investment report, and investment efficiency of investment in the investment link, which makes it widely concerned in the society and in enterprises.

A multistart and recombination algorithm for finding many unique solutions to spatial aggregation problems

AbstractSpatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their computational intensity. In addition, these problems often have multiple, instead of singular, optimal solutions that have the same or similar objective function values but exhibit different spatial configurations. Existing solution methods often aim to find single solutions to these problems. In this paper, we discuss a new heuristic method that can be used to find a set of unique optimal or near-optimal solutions to spatial aggregation problems. The algorithm consists of two phases. A multistart phase first generates a pool of random solutions to a problem. The size of the pool is specified by the user and contains the number of solutions desired to be found. Each random solution is then improved using an efficient algorithm called give-and-take. The second phase uses a recombination algorithm to create new solutions based on solutions randomly selected from the pool. The worst solution in the pool will be replaced by the new solution if the latter is better and does not exist in the pool. We test this multistart and recombination algorithm (MSRA) using a variety of problems with different sizes and the results suggest the effectiveness of the algorithm in finding multiple unique optimal or near-optimal solutions.

Flow control method in concrete particles pneumatic sweeping process based on image recognition and OPC protocol

Based on image recognition and OPC(Object linking and embedding for Process Control) protocol, an intelligent pneumatic sweeping scheme is proposed to solve the problem of pavement aggregates cleaning. Firstly, the effects of four different auxiliary light sources and six different image segmentation methods on the recognition of single particle images were analyzed, and the maximum entropy segmentation method was the best under white light. Secondly, the effect of four different filtering methods on discrete multi-particle image recognition was studied. Among them, the adaptive median filter can better solve the problem of interference points misidentified as particles. The recognition effect of four different edge detection methods on aggregate particle pile was analyzed. And Canny operator with better effect was finally used to recognize particle pile from the overhead and side view. Finally, the communication mode between the host computer and the controller established based on OPC protocol was verified by experiments.

Managing and aggregating group evidence under quality and quantity trade-offs

Trade-offs between quality and quantity arise in an abundance of contexts concerning group decision making. With the starting point being that group members provide more accurate evidence when they are involved with fewer tasks, team managers often encounter the following dilemma: Should they assign their group members with many tasks (attempting to gather more evidence with lower quality), or with fewer tasks (aiming at receiving less, but more high-quality evidence)? Secondly, what is the optimal way to aggregate the collected evidence from a group, which may be contrasting and varying in accuracy? Should more weight be given to the more accurate group members, or to the larger number of those who provide the same answer? This topic is already studied within the mathematical framework of Terzopoulou and Endriss (2019). In this paper we complement it experimentally, by investigating to what extent people's decision-making patterns are in accordance with the optimal ones proposed by the normative model. Our findings suggest that people understand the task at hand and generally opt for optimal choices, especially in conflict-free cases. Still, a tendency towards overvaluing the importance of additional evidence, despite their accuracy, is observed; this translates into choosing options that align with the majority rule in aggregation problems.

Ti3C2Tx MXene@metal-organic frameworks-derived bead-like carbon nanofibers heterostructure aerogel for enhanced performance lithium/sodium storage

MXene has shown remarkable performance in constructing stable interfacial interactions with high metal-like conductivity and abundant surface functional groups for Li+/Na+ storage. However, traditional MXene nanosheets face challenges in commercial applications for lithium/sodium-ion batteries (LIBs/SIBs) due to aggregation and self-stacking problems. In this study, we introduced a novel porous Ti3C2Tx MXene-based heterostructure aerogel with a unique metal-organic frameworks (MOF)-derived bead-like structure. This structure is composed of MIL-88 A along the radial alignment of N-doping carbon nanofibers and layer Ti3C2Tx MXene nanosheets through a process involving electrospinning, in-situ growth, calcination and freeze-drying methods. The resulting MOF-derived bead-like structure of the heterostructure aerogel composite enables fast ion/electron diffusion and provides structural durability, thereby alleviating the accumulation of MXene nanosheets. This innovative design addresses volume expansion concerns and offers additional channels for Li+/Na+ transport. As a consequence, the obtained MOF-Fe2O3@carbon@Ti3C2Tx MXene composite nanofibers show high-rate performance, achieving 202 mAh g−1 at 10 A g−1 for LIBs and 98 mAh g−1 at 5 A g−1 for SIBs. The excellent cycling performance is observed with a capacity of 401 mAh g−1 at 2 A g−1 after 2000 cycles for LIBs and 197 mAh g−1 at 1 A g−1 after 1000 cycles for SIBs.

The effect of processing/curing temperature and viscosity of epoxy resins on the flexural/tensile properties of GNP-epoxy resin nanocomposites

Despite the problem of graphene aggregation in cured epoxy resin, no studies have clarified the tendency of graphene aggregation in a wide variety of material systems and process. We hypothesized that the use of epoxy resins at high temperatures leads to an extreme decrease in resin surface tension and viscosity of epoxy, which would cause re-aggregation of graphene once dispersed. So, we examined process/material with/without high temperature treatment and epoxy resins of different viscosities, and properties of nanocomposites were evaluated by flexural/tensile test. Experiments revealed that the process not including high temperature treatment suppressed graphene re-aggregation and maintained the high mechanical properties of nanocomposites. In particular, the increased tensile modulus by 18.44% and flexural strength by 2.81% (@ 1 wt% graphene loading), which were quite high for non-functionalized graphene, were recorded when epoxy was treated and cured at 20 ˚C at all times to keep the surface tension of epoxy desirable.

Designing MOFs/CuCo-LDHs hybrid-based superhydrophobic sponge for speedy eliminating microplastics, dyes, and oils from high-salinity water

It is urgent to develop effective materials to decontaminate wastewater owing to water scarcity in the whole world. In this study, we successfully fabricated a superhydrophobic and superoleophilic MOFs/LDHs based-functional sponge (OMCTS/ZIF-67/CuCo-LDHs@Sponge) with large specific surface area and high porosity for the first time through a two-step co-precipitation method and surface modification of polysiloxane. The polydopamine (PDA) molecules not only greatly improved the adhesion between coatings and substrate, but also enhanced growth of MOFs and LDHs structure. The in-situ growth of hexahedron ZIF-67 particles on multiple layered CuCo-LDHs sphere formed heterojunction, which significantly increased adsorption sites, specific area, and porosity and also solved the aggregation problem of powder MOFs. The CuCo-LDHs with large surface area was beneficial to ZIF-67 growth owing to same Co2+connection. The developed superhydrophobic sponge not only quickly and selectively absorbed various microplastics (MPs, as high as 500 mg/mL), dyes (C0 = 40 mg/L, removal rate of 99.6 %), and emulsified oils from high-salinity water with high capacity (e.g. 85 mg/g for PP-2000, 94 mg/g for PS-100, and 93 mg/g for PE-2000), but also exhibited outstanding synchronous decontamination capability for coexisting pollutants through multi-molecular synergistic mechanism and multiple adsorption forces (grasping force, strong capillarity, electrostatic interaction, hydrogen bonds, π-π interactions, p-π conjugation interaction. Van der Waals forces). The coated sponge showed superior mechanical durability, which still maintained separation ability above 98 % after repeated adsorption of MPs for 40 cycles. The adsorption mechanism was detailly investigated through theoretical simulation of adsorption energy. This coated sponge was destined to show broad and useful applications in water purification field.

Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability

The virtual power plant (VPP) provides an effective way for the coordinated and optimized operation of distributed energy resources (DERs). To solve the aggregation problem of a VPP containing scattered layouts and heterogeneous performance DERs, this study proposes a dynamic aggregation strategy to improve the flexible regulation ability of the VPP. A VPP aggregation model considering network constraints and temporal coupling constraints of DERs is constructed, while some VPP performance parameters are proposed to characterize and quantify the regulation ability. An uncertainty set considering time-series coupling properties of variables is constructed, and an aggregation method under uncertainty scenarios is proposed based on a two-stage robust optimization model. In addition, a dynamic aggregation strategy is proposed for the application of VPPs in electricity markets. Finally, case studies demonstrates that the proposed method provides a more extensive feasible region compared to other methods, and the deviation power remains within a reasonable range. The dynamic aggregation strategy facilitates the synergistic and correlative operation of DERs, exploits the regulation ability of the VPP in frequency regulation and spinning reserve, and improves the feasibility of practical applications. Simultaneously, the income of the VPP increased by 29.5 %.

What can we learn from industry-level (aggregate) production functions?

ABSTRACT Recent work has revived two intertwined challenges to aggregate production functions (the ‘identity’ and ‘aggregation’ problems). This paper examines both problems in the context of aggregate industry-by-country analysis, first demonstrating the relevance of the identity problem for industry-level analysis and tracing its origin in the System of National Accounts. Using a case study of materials quality in global forestry and logging, the paper then compares estimates from fully physical versus conventional (monetary) production functions to isolate the aggregation problem and show that credible inference depends on appropriately modelling heterogeneity in production processes. Materials quality is measured via finite mixture modelling applied to global satellite data. Attempting to estimate the parameters of a common production technology yields poor results, because of differences in production processes between countries. The paper offers a practical approach for dealing with heterogeneity via Data Envelopment Analysis and heterogeneous coefficient panel estimators, and concludes with guidance to help applied industry-level analysis recognize and avoid both the identity and aggregation problems.

A critical review of bench aggregation and mining cut clustering techniques based on optimization and artificial intelligence to enhance the open-pit mine planning

Determining the mining sequence is one of the main objectives in mine planning. However, depending on the size of the analyzed instances, such activity might become an extremely difficult task, despite current computational capacity. In addition, determining a feasible and operational mining sequence is also challenging, so practitioners usually employ strategies to segment and simplify the main problem, such as splitting it into distinct time horizons and aggregating blocks into clusters. This paper aims to perform a critical review about the different clustering methodologies and algorithms used for mining-block aggregation, with the purpose of understanding the proposed solutions and identifying the gaps found in the current literature. The reviewed aggregation strategies encompass the modelling of tabular deposits as sets of layers and grouping of blocks in benches, bench-phases, and mining cuts. Among the optimization techniques evaluated, one may find heuristics, artificial intelligence, and exact approaches, relying on deterministic or uncertainty-based methodologies, considering approximately six decades of studies and covering fifty-eight works published in journals and proceedings from 1967 to 2022. In addition to what is seen within the literature analyzed, we also propose future research directions, such as approaches and algorithms not yet implemented to solve the block aggregation problem, thus presenting opportunities for further research in this field.

Simulation and analysis of enhancement-mode AlGaN/GaN HEMT with P-I-N junction gate

To improve the threshold voltage and gate reliability of conventional enhancement-mode p-GaN-gated AlGaN/GaN high electron mobility transistors while maintaining a low on-resistance, an improved design solution for p-GaN HEMTs with P-I-N junction gate (PIN-HEMTs) has been proposed. Simulation results show that energy band modulation is achieved by adjusting the doping concentration and thickness of each layer of the PIN junction, and high-performance p-GaN gate HEMTs with adjustable threshold voltages ranging from 0.56 V to 4.75 V and gate breakdown voltages ranging from 19.8 V to 30.3 V would be prepared. The PIN-HEMT has a quasi-self-alignment property, which means that good gate control is independent of gate metal alignment. This not only improves the production efficiency but also solves the problems of weak gate control and electric field aggregation at the gate edge caused by the gate misalignment in conventional p-GaN gate HEMTs, thus realizing lower on-resistance and higher gate breakdown voltage, which demonstrates this proposed structure has excellent potentials for realizing effective and reliable high-power transistors.

Local maximum synchrosqueezing reassigning chirplet transform and its application to gearbox fault diagnosis

Abstract Aiming at the problems of poor time-frequency aggregation and severe noise interference when traditional time-frequency analysis methods deal with complex multi-component signals, this paper proposes a new time-frequency analysis method – local maximum synchrosqueezing reassigning chirplet transform (LMSRCT). The core idea of the method is to introduce the principle of general linear chirplet transform (GLCT) into synchro-reassigning transform (SRT), followed by reassigning the results of local maximum synchrosqueezing chirplet transform (LMSSCT), and then introducing the innovative synchro-reassigning operator (SRO). This results to a novel three-step method for extracting instantaneous frequency, which ultimately yields the final time-frequency representation. This method ensures the integrity of each instantaneous frequency, solves the problem of energy divergence problems, and improves aggregation. Simulation and experimental results show that the LMSRCT can provide better characterization results compared to other methods and can effectively solve the different situations that occur between the instantaneous frequencies of complex signals. The proposed method can effectively estimate transmission speed, which achieves fault diagnosis under tacholess conditions, and the order analysis results are more accurate and reliable.

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks.

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

Robust Distributed Gradient Aggregation Using Projections onto Gradient Manifolds

We study the distributed gradient aggregation problem where individual clients contribute to learning a central model by sharing parameter gradients constructed from local losses. However, errors in some gradients, caused by low-quality data or adversaries, can degrade the learning process when naively combined. Existing robust gradient aggregation approaches assume that local data represent the global data-generating distribution, which may not always apply to heterogeneous (non-i.i.d.) client data. We propose a new algorithm that can robustly aggregate gradients from potentially heterogeneous clients. Our approach leverages the manifold structure inherent in heterogeneous client gradients and evaluates gradient anomaly degrees by projecting them onto this manifold. This algorithm is implemented as a simple and efficient method that accumulates random projections within the subspace defined by the nearest neighbors within a gradient cloud. Our experiments demonstrate consistent performance improvements over state-of-the-art robust aggregation algorithms.

Project-Fair and Truthful Mechanisms for Budget Aggregation

We study the budget aggregation problem in which a set of strategic voters must split a finite divisible resource (such as money or time) among a set of competing projects. Our goal is twofold: We seek truthful mechanisms that provide fairness guarantees to the projects. For the first objective, we focus on the class of moving phantom mechanisms, which are -- to this day -- essentially the only known truthful mechanisms in this setting. For project fairness, we consider the mean division as a fair baseline, and bound the maximum difference between the funding received by any project and this baseline. We propose a novel and simple moving phantom mechanism that provides optimal project fairness guarantees. As a corollary of our results, we show that our new mechanism minimizes the L1 distance to the mean for three projects and gives the first non-trivial bounds on this quantity for more than three projects.

Approximation Algorithms for Preference Aggregation Using CP-Nets

This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). Its focus is on aggregating preferences over so-called swaps, for which optimal solutions in general are already known to be of exponential size. We first analyze a trivial 2-approximation algorithm that simply outputs the best of the given input preferences, and establish a structural condition under which the approximation ratio of this algorithm is improved to 4/3. We then propose a polynomial-time approximation algorithm whose outputs are provably no worse than those of the trivial algorithm, but often substantially better. A family of problem instances is presented for which our improved algorithm produces optimal solutions, while, for any ε, the trivial algorithm cannot attain a (2- ε)-approximation. These results may lead to the first polynomial-time approximation algorithm that solves the CP-net aggregation problem for swaps with an approximation ratio substantially better than 2.

Construction of copper-functionalized two-dimensional zeolitic imidazole framework-7 (ZIF-7) nanosheets for enhanced antibacterial and antibiofilm performance

Marine biological pollution restricts the sustainable development of marine economy. Cu2O stands as a predominant antifouling agent for developing marine resources engineering. However, the problems of easy aggregation, rapid failure, and explosive release of copper ions limit the practical application of Cu2O. In this work, a novel copper-functionalized two-dimensional zeolitic imidazolate framework-7 nanocomposites ZIF-7@Cu2O was successfully fabricated by in-situ growth methodology. The emergence of ZIF-7@Cu2O nanocomposites solves the above problems in the use of Cu2O. Due to the huge specific surface area of the 2D ZIF-7 nanosheets and the electrostatic interactions, the cubic Cu2O, with a diameter of 250 nm, can be uniformly and firmly anchored on the surface of 2D ZIF-7 nanosheets, thus effectively avoiding the rapid dissolution of cuprous oxide and reducing the release rate of copper ions. Antibacterial experiments demonstrate that, in comparison to ZIF-7, Cu2O, and ZIF-7/Cu2O (physical mixture of ZIF-7 and Cu2O), ZIF-7@Cu2O exhibits superior antibacterial properties with 99.99 % against E. coli and the minimum inhibitory concentration (MIC) of 32 μg/mL. Furthermore, the reactive oxygen species (ROS) assay confirms ZIF-7@Cu2O could promote the generation of ROS. Antibiofilm formation results reveal that ZIF-7@Cu2O nanocomposites are effective in preventing biofilm formation of E. coli and P. aeruginosa at a low concentration (50 μg/mL). Finally, ZIF-7@Cu2O nanocomposites are employed as fillers to prepare ZIF-7@Cu2O/EP composite coating, the bactericidal kinetics and bacterial adherence on the coating surface are investigated. We believe this work could provide novel insights into the rational design of 2D ZIF-based material in the field of antifouling.

Transforming clay minerals into solvent-free nanofluids via an interfacial interaction: An experimental and simulation study

The emergence of solvent-free nanofluids (SFNs) has solved the problem of aggregation of conventional nanoparticles, resulting in better compatibility and dispersion of nanoparticles, and has led to the rapid development of their applications in various fields. However, at present, clay mineral-based SFNs (Clay-F) are rarely reported, and the general preparation method of clay mineral-based SFNs has not been investigated. Therefore, we report a general strategy for the construction of Clay-F. Clay-F have good flow properties at room temperature, and have higher dispersion stability under the steric hindrance effect of the long chain of the corona-canopy species. We demonstrated the universality of this method by grafting a corona-canopy species onto various clay minerals (montmorillonite, kaolinite, and attapulgite). At the same time, based on the lack of current research on the synthesis mechanism of Clay-F by simulation, the interaction mechanism between montmorillonite and corona-canopy species was finally confirmed and verified at the molecular level by quantum chemistry and molecular dynamics simulation, and the formation mechanism of Clay-F was further determined. Finally, to verify the concept, the adsorption performance of Clay-F for CO2 was investigated. This work expands the family of solvent-free nanofluids and provides new ideas for the application of clay minerals.