Exhaustive Search Method Research Articles

An inner boundary condition for solar wind models based on coronal density

Accurate forecasting of the solar wind has grown in importance as society becomes increasingly dependent on technology susceptible to space weather events. This work describes an inner boundary condition for ambient solar wind models based on tomography maps of the coronal plasma density gained from coronagraph observations, providing a novel alternative to magnetic extrapolations. The tomographical density maps provide a direct constraint of the coronal structure at heliocentric distances of 4–8R⊙, thus avoiding the need to model the complex non-radial lower corona. An empirical inverse relationship converts densities to solar wind velocities, which are used as an inner boundary condition by the Heliospheric Upwind Extrapolation (HUXt) model to give ambient solar wind velocity at Earth. The dynamic time warping (DTW) algorithm is used to quantify the agreement between tomography/HUXt output andin situdata. An exhaustive search method is then used to adjust the lower boundary velocity range in order to optimise the model. Early results show up to a 32% decrease in mean absolute error between the modelled and observed solar wind velocities compared to the coupled MAS/HUXt model. The use of density maps gained from tomography as an inner boundary constraint is thus a valid alternative to coronal magnetic models and offers a significant advancement in the field, given the availability of routine space-based coronagraph observations.

An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

Non-orthogonal multiple access (NOMA) has been a key enabling technology for the fifth generation (5G) cellular networks. Based on the NOMA principle, a traditional neural network has been implemented for user clustering (UC) to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones. Consequently, the prediction of UC for the future ones is based on the current clustering information, which is never used again due to the lack of memory of the network. Therefore, to relate the input features of NOMA users and capture the dependency in the clustering information, time-series methods can assist us in gaining a helpful insight into the future. Despite its mathematical complexity, the essence of time series comes down to examining past behavior and extending that information into the future. Hence, in this paper, we propose a novel and effective stacked long short term memory (S-LSTM) to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems. In the proposed strategy, the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells. The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers. Specifically, we develop, train, validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data. Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94% compared to the exhaustive search method.

Yarım Bant Nyquist Filtre ile Sinyallerin Ayrıştırılması ve Gürültünün Giderilmesi Üzerine Bir Araştırma

Medical signal processing is often used for analyzing and detecting diseases from bio-signals. Segmentation, filtering, and noise removal are application examples in this area. The segmentation or decomposition of signals is investigated in this study. Each branch of two band quadrature-mirror orthogonal filter bank structure (analysis and synthesis parts are cascaded) is a half band Nyquist filter when down-up samplers are removed. To perform partitioning, one branch of the filter bank was adapted to a component of input signal to suppress the other element and filter out the target part of the signal. This was done by obtaining the filter-tap weights, which minimize - norm of error at the output. This goal was achieved by employing the exhaustive search method. The filter performance was tested for three scenarios: filtering with and without down-up samplers and wavelet de-noising using one-level decomposition. The comparison was done with Daubechies and Symlet filters. The approach was run and tested for separating synthetically generated evoked potential from noise, and the results show that the designed filter achieves lower total absolute error than the classical wavelet filters.

Maximizing the Set Cardinality of Users Scheduled for Ultra-Dense uRLLC Networks

Ultra-reliability and low latency communication has long been an important but challenging task in the fifth and sixth generation wireless communication systems. Scheduling as many users as possible to serve on the limited time-frequency resource is one of a crucial topic, subjecting to the maximum allowable transmission power and the minimum rate requirement of each user. We address it by proposing a mixed integer programming model, with the goal of maximizing the set cardinality of users instead of maximizing the system sum rate or energy efficiency. Mathematical transformations and successive convex approximation are combined to solve the complex optimization problem. Numerical results show that the proposed method achieves a considerable performance compared with exhaustive search method, but with lower computational complexity.

Learning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks

The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Adopting full-pilot zero-forcing at access points (APs) to comprehensively assess the system performance, we formulate the sum SE optimization problem taking into account power constraints at APs, necessary conditions for implementing successive interference cancellation, and required SE constraints at user equipments. The formulated optimization problem is highly non-convex, and thus, it is difficult to obtain the global optimal solution. Therefore, we develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated.

A new structure optimization method for forced air-cooling system based on the simplified multi-physics model

Energy storage systems equipped with lithium-ion batteries are susceptible to fire and explosion hazards, especially when such batteries are used to power electric vehicles. One of the most important reasons for these undesirable consequences is the lack of an effective battery thermal management system. In this regard, an optimization method for the forced air-cooling system is introduced based on a simplified multi-physics simulation model. The simplified multi-physics model consists an electro-thermal coupling model and a flow resistance network model. The accuracy of the simplified multi-physics model is verified by comparison with experimental results. Subsequently, the deflector angle and cell space in the forced air-cooling system are optimized, respectively, based on the simplified multi-physics model, via an exhaustive search method and a genetic algorithm. Finally, the verification platform for the forced air-cooling system is built. After the optimization of deflector angle and cell space, the maximum temperature and temperature difference are limited to 38℃ and 2℃, with a reduction of about 30% and 80%, with respect to the unoptimized version. The consistency between the simulated and measured maximum temperature further confirms the effectiveness of the structure optimization method. In that, the method proposed in this paper is of great significance to shorten the time of structural optimization for forced air-cooling systems.

Hierarchical beam alignment in SU-MIMO terahertz communications

Single-Carrier Frequency Division Multiple Access (SC-FDMA) is a promising technique for high data rate indoor Terahertz (THz) communications in future beyond 5G systems. In an indoor propagation scenario, the Line-Of-Sight (LOS) component may be blocked by the obstacles. Thus, efficient THz SC-FDMA communications require a fast and reliable Beam Alignment (BA) method for both LOS and Non-Line-Of-Sight (NLOS) scenarios. In this paper, we first adopt the hierarchical discrete Fourier transform codebook for LOS BA, and introduce the hierarchical k-means codebook for NLOS BA to improve the beamforming gain. Simulation results illustrate that the hierarchical DFT codebook and the hierarchical k-means codebook can achieve the beamforming gain close to that of the maximum ratio transmission in LOS and NLOS cases, respectively. Based on these two codebooks, we propose a Multi-Armed Bandit (MAB) algorithm named Hierarchical Beam Alignment (HBA) for single-user SC-FDMA THz systems to reduce the BA latency. HBA utilizes a hierarchical structure in the adopted codebook and prior knowledge regarding the noise power to speed up the BA process. Both theoretical analysis and simulation results indicate that the proposed BA method converges to the optimal beam with high probability for both the hierarchical DFT codebook and the hierarchical k-means codebook in the LOS and NLOS scenarios, respectively. The latency introduced by HBA is significantly lower when compared to an exhaustive search method and other MAB-based methods.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

An Effective Algorithm for the Synthesis of Irreducible Polynomials over a Galois Fields of Arbitrary Characteristics

The known algorithms for synthesizing irreducible polynomials have a significant drawback: their computational complexity, as a rule, exceeds the quadratic one. Moreover, consequently, as a consequence, the construction of large-degree polynomials can be implemented only on computing systems with very high performance. The proposed algorithm is base on the use of so-called fiducial grids (ladders). At each rung of the ladder, simple recurrent modular computations are performers. The purpose of the calculations is to test the irreducibility of polynomials over Galois fields of arbitrary characteristics. The number of testing steps coincides with the degree of the synthesized polynomials. Upon completion of testing, the polynomial is classifieds as either irreducible or composite. If the degree of the synthesized polynomials is small (no more than two dozen), the formation of a set of tested polynomials is carried out using the exhaustive search method. For large values of the degree, the test polynomials are generating by statistical modeling. The developed algorithm allows one to synthesize binary irreducible polynomials up to 2Kbit on personal computers of average performance

An Analysis of Valid Nodes Distribution for Sphere Decoding in the MIMO Wireless Communication System

Sphere Decoding (SD) algorithms can achieve a quasi-maximum likelihood (ML) decoder performance over Gaussian multiple input-multiple output (MIMO) channels with much lower complexity compared to the exhaustive search method. The SD algorithm is based on a closest lattice point search over a limited search space (hypersphere). On top of that, QR-decomposition simplifies the SD linear system's matrix to be an upper triangle matrix. The solution solver then is done by searching in the exponentially expanding search tree, started from the top with only a single node then increases by M times every level (in $N_T\times N_R$ MIMO system). Fortunately, the SD algorithm shrinks its hypersphere at every level (once the level node is determined) and phases out a vast number of the candidates, remaining only specific valid nodes in the current considered level. In this work, we proposed the statistical approach for evaluating the adequate number of valid search nodes at every level of the search tree, aiming to optimize the overall computational workload. We use a massive number of inputs patterns and extensive simulation to project the number of remaining valid nodes during the searching process. The simulations have been conducted for $4\times 4$ and $8\times 8$ MIMO systems. Our results indicate that for a particular targeted BER, choosing an appropriate sphere radius is essentially important and the number of necessary calculations increases only at the middle layer and can be generically quantified regardless of the system characteristics. This finding is beneficial for the hardware implementation of the SD, where the number of computational units has to be fixed in advance.

Machine learning workflow for the oil uptake prediction of rice flour in a batter-coated fried system

Machine learning algorithms were applied to predict the oil uptake of rice flour in batter-coated fried foods, depending on its physicochemical features before frying (amylose content, pasting parameters, and batter viscosity). Low coefficients of Pearson correlation (0.29–0.61) were observed between the oil uptake of rice frying batters and other physicochemical parameters. Based on an exhaustive search method by the regsubsets function, the four experimental features (amylose content, peak time, peak temperature, and final viscosity) were selected as the best subset to affect the oil uptake of rice batters after frying, and then subjected to two machine learning algorithms – multivariable linear regression and multilayer perceptron neural network. Based on K-fold cross-validation, the experimental results were divided into 5 datasets consisting of each 80% training and 20% testing dataset. Compared to the multivariable linear regression (R2 = 0.6204–0.7219), the iterative application of the multilayer perceptron model made a relatively higher prediction (R2 = 0.7388–0.7781) of the oil uptake of rice flour frying batter. Thus, the multilayer perception model with a hidden layer outperformed the multivariable linear regression by showing higher R2 and lower relative error.

Coalitional Game-Based Cooperative Computation Offloading in MEC for Reusable Tasks

Mobile-edge computing (MEC) has been a promising solution for Internet-of-Things (IoT) applications to obtain latency reduction and energy savings. In view of the loosely coupled application, multiple devices can use the same task code and different input parameters to obtain diverse results. This motivates us to study the cooperation between devices for eliminating the repeated data transmission. Leveraging coalitional game theory, we formalize the cooperative offloading process of a reusable task into a coalitional game to maximize the cost savings. In particular, we first propose an efficient coalitional game-based cooperative offloading (CGCO) algorithm for the single-task model, and then expand it into a CGCO-M algorithm for the multiple-task model with jointly applying a two-stage flow shop scheduling approach, which helps to obtain an optimal task schedule. It is proved that our CGCO and CGCO-M can achieve the Nash-stable solution with convergence guarantee, and CGCO can obtain an optimal solution. The simulations show that CGCO is equal to the optimal exhaustive search (ES) method and CGCO-M is close to ES in terms of cost ratios. Cost ratios of CGCO and CGCO-M are significantly down by 41.08% and 83.70% compared to local executions, respectively. Meanwhile, CGCO-M obtains 41.46% and 89.74% reductions when reuse factors are 0.1 and 1, which means CGCO-M can save more cost with higher reuse density.

Analysis and Optimization of Truss Structures, Constrained Handling using Genetic Algorithm

In this study, an attempt is made to minimize the weight of Howe roof and ten member-6 Node trusses, separately. Two constraints, maximum allowable deflection and maximum allowable member stresses have been considered. For the first truss, permissible deflection is not known from the literature; therefore, it is determined using the exhaustive search method. Once magnitudes of the constraints are identified, member cross-sectional areas are varied to get the optimal weight. Both the exhaustive search method and the genetic algorithm have been implemented for this purpose. During the optimization, members tending to form a string may be eliminated from the structure. Doing this, we could further reduce the weights of the trusses and even less than the minimum available in the literature. The second truss is an indeterminate structure, and Maxwell Betti reciprocal theorem is applied to calculate the member forces. Also, further reduction of members is made for this truss, keeping in mind that the truss becomes determinate with the decrease in the member(s).

Differential Fault Attack on GIFT

GIFT, a lightweight block cipher proposed at CHES2017, has been widely cryptanalyzed this years. This paper studies the differential diffusion characteristics of round function of GIFT at first, and proposes a random nibble-based differential fault attack. The key recovery scheme is developed on the statistical properties we found for the differential distribution table of the S-box. A lot of experiments had been done and experimental results show that one round key can be retrieved with an average of 20.24 and 44.96 fault injections for GIFT-64 and GIFT-128 respectively. Further analysis shows that a certain number of fault injections recover most key bits. So we demonstrate an improved fault attack combined with the method of exhaustive search, which shows that the master key can be recovered by performing 216 and 217 computations and injecting 31 and 32 faults on an average for GIFT-64 and GIFT-128 respectively.

Solubility and effective diffusion coefficient of CO2 in fresh cheese (type Minas Frescal)

AbstractCarbon dioxide (CO2) is the most important gas used in modified atmosphere packaging of nonrespiring foods. In this technique, the gas solubilizes into the aqueous and lipid phases of food and exerts an antimicrobial effect. In the present study, the solubility and effective diffusion coefficient of CO2 in fresh cheese (type Minas Frescal) were determined at 4, 7, and 10°C by a manometric method. The value was obtained using the mathematical modeling (transient mass transfer and Fick's law) and a numerical procedure (exhaustive search method) to minimize the error between experimental and predicted values. The highest solubility value was obtained at 4°C, resulting from combined effects of temperature and complex phase transitions in fat content, shown by differential scanning calorimetry curves. Higher values corresponded to lower . Solubility and effective diffusion coefficient are important parameters for designing and developing new food products and food processes.Practical ApplicationsThis study presented a procedure to quantify the mass transfer of CO2 gas in fresh cheese. The knowledge of the CO2 transfer properties in food can help the industry concerning about the choice of the amount and composition of the gas used in modified atmosphere packaging. In which, a gaseous mixture (CO2/N2) is introduced into packaging resulting in the extension of the cheese shelf life. Additionally, the procedure used in cheese can be easily extended to estimate the transfer properties of CO2 in other nonrespiring food.

Heat transfer efficiency of hierarchical corrugated sandwich panels

As a kind of biomimetic materials, ultralight hierarchical porous structures possessing excellent mechanical properties such as load bearing, impact energy absorption, vibration reduction and noise attenuation have been exploited. Often, these porous structures exhibit continuous flow passages that allow for cooling fluids to pass through, thus enabling simultaneous load-bearing and active heat dissipation. This study investigated the convective cooling efficiency of a sandwich panel with hierarchical corrugated core subjected to heating from the face sheets and active cooling through the core. Built upon the classical fin approach, a theoretical model coupling wall heat conduction and fluid convection in the core was established for the hierarchical corrugated-core sandwich panel, covering the full range of fluid flow (from laminar, transition to turbulent). The theoretical model predictions were validated against full numerical simulations. Based on the theoretical model, an artificial intelligence optimization method (i.e., the ant colony algorithm) was adopted to find the optimal combination of key independent geometric parameters of the sandwich panel for maximized heat transfer performance. For the problem of multi-variables optimization, it was demonstrated that the ant colony algorithm is superior in terms of computational time to the traditional exhaustive search method. For a given pressure drop, a set of optimum geometric parameters corresponding to maximum cooling efficiency was found for the proposed hierarchical corrugated-core sandwich panel.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.

Improving Causal Discovery By Optimal Bayesian Network Learning

Many widely-used causal discovery methods such as Greedy Equivalent Search (GES), although with asymptotic correctness guarantees, have been reported to produce sub-optimal solutions on finite data, or when the causal faithfulness condition is violated. The constraint-based procedure with Boolean satisfiability (SAT) solver, and the recently proposed Sparsest Permutation (SP) algorithm have shown superb performance, but currently they do not scale well. In this work, we demonstrate that optimal score-based exhaustive search is remarkably useful for causal discovery: it requires weaker conditions to guarantee asymptotic correctness, and outperforms well-known methods including PC, GES, GSP, and NOTEARS. In order to achieve scalability, we also develop an approximation algorithm for larger systems based on the A* method, which scales up to 60+ variables and obtains better results than existing greedy algorithms such as GES, MMHC, and GSP. Our results illustrate the risk of assuming the faithfulness assumption, the advantages of exhaustive search methods, and the limitations of greedy search methods, and shed light on the computational challenges and techniques in scaling up to larger networks and handling unfaithful data.

A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH4 and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Constructal Design and Simulated Annealing applied to the geometric optimization of an isothermal Double T-shaped cavity

Present numerical work comprises the application of Constructal Design (CD) and Simulated Annealing (SA) for the geometric optimization of a Double T-shaped cavity inserted into a solid wall with internal heat generation. The main goal is to minimize the dimensionless maximum excess of temperature in the solid domain. The cavity is subjected to four constraints and five degrees of freedom (DOF). The Exhaustive Search (ES) and SA methods are employed to optimize the first three DOFs, validating the SA algorithm. From the fourth level onward, new recommendations about the design influence over the system's performance are reached, combining ES and different versions of SA. Results obtained showed the importance of investigating the influence of the DOFs over thermal performance, and corresponding design, using combined SA versions and ES. The proposed methodology reduced the computational effort in comparison with purely ES. For design investigation, the highest insertion of stem and branches led to the best performance. For square solid domains, the Double T-shaped cavity obtained an intermediate performance compared with I, T, Y, H, and ψ shapes. For small height/length solid domain ratios, the best shape found here performed several times better than the best configurations found in the literature.