Brute Force Method Research Articles

Heuristic Method for Minimizing Model Size of CNN by Combining Multiple Pruning Techniques.

Network pruning techniques have been widely used for compressing computational and memory intensive deep learning models through removing redundant components of the model. According to the pruning granularity, network pruning can be categorized into structured and unstructured methods. The structured pruning removes the large components in a model such as channels or layers, which might reduce the accuracy. The unstructured pruning directly removes mainly the parameters in a model as well as the redundant channels or layers, which might result in an inadequate pruning. To address the limitations of the pruning methods, this paper proposes a heuristic method for minimizing model size. This paper implements an algorithm to combine both the structured and the unstructured pruning methods while maintaining the target accuracy that is configured by its application. We use network slimming for the structured pruning method and deep compression for the unstructured one. Our method achieves a higher compression ratio than the case when the individual pruning method is applied. To show the effectiveness of our proposed method, this paper evaluates our proposed method with actual state-of-the-art CNN models of VGGNet, ResNet and DenseNet under the CIFAR-10 dataset. This paper discusses the performance of the proposed method with the cases of individual usage of the structured and unstructured pruning methods and then proves that our method achieves better performance with higher compression ratio. In the best case of the VGGNet, our method results in a 13× reduction ratio in the model size, and also gives a 15× reduction ratio regarding the pruning time compared with the brute-force search method.

Experimental demonstration of a fast calibration method for integrated photonic circuits with cascaded phase shifters

With the development of research on integrated photonic quantum information processing, the integration level of the integrated quantum photonic circuits has been increasing continuously, which makes the calibration of the phase shifters on the chip increasingly difficult. For the calibration of multiple cascaded phase shifters that is not easy to be decoupled, the resources consumed by conventional brute force methods increase exponentially with the number of phase shifters, making it impossible to calibrate a relatively large number of cascaded phase shifters. In this work, we experimentally validate an efficient method for calibrating cascaded phase shifters that achieves an exponential increase in calibration efficiency compared to the conventional method, thus solving the calibration problem for multiple cascaded phase shifters. Specifically, we experimentally calibrate an integrated quantum photonic circuit with nine cascaded phase shifters and achieve a high-precision calibration with an average fidelity of 99.26%.

Train Speed Profile Optimization for Energy Saving

This study aims to optimize energy consumption by modifying the train’s maximal speed and coasting velocity. The methods used in the simulation are brute force and genetic algorithm (GA). The introduction briefly introduces the aim and objectives of the study, as well as the scope and the methodology. The following section gives an overview of the current rail transit development and the existing issues. Despite the rapid development of rail transit and its successful operation, energy consumption is a major issue. The methodology of brute force and genetic algorithm is then introduced. The exact algorithm of the two methods in MATLAB is explained so as to make preparations for the latter simulation optimization. The results from the brute force and genetic algorithm methods are obtained and compared for data analysis. The driving strategy for using STS (Single Train Simulator) is then optimized for an advanced modification. By inserting more values in the code, an optimal speed profile is obtained, and the energy saving target is achieved. Overall, the energy consumption of the studied line could be decreased by optimizing the maximal speed of different sections between the stations and the coasting velocity. However, influencing factors such as service and infrastructure, application of acceleration, and braking power should also be considered as improvements in future studies.

A review of the technology and applications of methods for evaluating the transport of air pollutants

A variety of methods based on air quality models, including tracer methods, the brute-force method (BFM), decoupled direct method (DDM), high-order decoupled direct method (HDDM), response surface models (RSMs) and so on forth, have been widely used to study the transport of air pollutants. These methods have good applicability for the transport of air pollutants with simple formation mechanisms. However, differences in research conclusions on secondary pollutants with obvious nonlinear characteristics have been reported. For example, the tracer method is suitable for the study of simplified scenarios, while HDDM and RSMs are more suitable for the study for nonlinear pollutants. Multiple observation techniques, including conventional air pollutant observation, lidar observation, air sounding balloons, vehicle-mounted and ship-borne technology, aerial surveys, and remote sensing observations, have been utilized to investigate air pollutant transport characteristics with time resolution as high as 1 sec. In addition, based on a multi-regional input-output model combined with emission inventories, the transfer of air pollutant emissions can be evaluated and applied to study the air pollutant transport characteristics. Observational technologies have advantages in temporal resolution and accuracy, while modeling technologies are more flexible in spatial resolution and research plan setting. In order to accurately quantify the transport characteristics of pollutants, it is necessary to develop a research method for interactive verification of observation and simulation. Quantitative evaluation of the transport of air pollutants from different angles can provide a scientific basis for regional joint prevention and control.

Automation of reversible steganographic coding with nonlinear discrete optimisation

Authentication mechanisms are at the forefront of defending the world from various types of cybercrime. Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management. Nevertheless, despite being generally imperceptible to human sensory systems, any degree of steganographic distortion might be inadmissible in fidelity-sensitive situations such as forensic science, legal proceedings, medical diagnosis and military reconnaissance. This has led to the development of reversible steganography. A fundamental element of reversible steganography is predictive analytics, for which powerful neural network models have been effectively deployed. Another core element is reversible steganographic coding. Contemporary coding is based primarily on heuristics, which offers a shortcut towards sufficient, but not necessarily optimal, capacity–distortion performance. While attempts have been made to realise automatic coding with neural networks, perfect reversibility is unattainable via such learning machinery. Instead of relying on heuristics and machine learning, we aim to derive optimal coding by means of mathematical optimisation. In this study, we formulate reversible steganographic coding as a nonlinear discrete optimisation problem with a logarithmic capacity constraint and a quadratic distortion objective. Linearisation techniques are developed to enable iterative mixed-integer linear programming. Experimental results validate the near-optimality of the proposed optimisation algorithm when benchmarked against a brute-force method.

Fast Determination of Satellite-to-Moon Visibility Using an Adaptive Interpolation Method Based on Vertex Protection.

Fast determination of satellite visibility with respect to a target area is important for satellite navigation and positioning. In this paper, we propose an adaptive interpolation algorithm based on vertex protection to solve the satellite visibility period problem more accurately and quickly, where “vertex” refers to the local extremum point. The algorithm can avoid the error in the visibility period calculation caused by skimming the vertices when fitting the multi-hump visibility function under certain fitting accuracy requirements with the traditional adaptive interpolation method. The algorithm does not need to construct a cubic polynomial in each subinterval to determine whether the satellite is visible or not; it only constructs a cubic polynomial to solve the problem if the visibility function of that subinterval is judged to have a solution from the existence theorem of zero points, which can improve the computational efficiency. For the lunar navigation problem, a solution to satellite–Moon visibility calculations based on a vertex-protected adaptive interpolation is given, and the experimental results show that the computation time of the algorithm can be reduced by approximately 98% compared with the brute force method and by approximately 30% compared with the traditional adaptive interpolation algorithm.

Operator backflow and the classical simulation of quantum transport

Tensor product states have proved extremely powerful for simulating the area-law entangled states of many-body systems, such as the ground states of gapped Hamiltonians in one dimension. The applicability of such methods to the dynamics of many-body systems is less clear: The memory required grows exponentially in time in most cases, quickly becoming unmanageable. New methods reduce the memory required by selectively discarding/dissipating parts of the many-body wave function which are expected to have little effect on the hydrodynamic observables typically of interest: For example, some methods discard fine-grained correlations associated with $n$-point functions, with $n$ exceeding some cutoff ${\ensuremath{\ell}}_{*}$. In this paper, we present a theory for the sizes of backflow corrections, i.e., systematic errors due to discarding this fine-grained information. In particular, we focus on their effect on transport coefficients. Our results suggest that backflow corrections are exponentially suppressed in the size of the cutoff ${\ensuremath{\ell}}_{*}$. Moreover, the backflow errors themselves have a hydrodynamical expansion, which we elucidate. We test our predictions against numerical simulations run on random unitary circuits and ergodic spin chains. These results lead to the conjecture that transport coefficients in ergodic diffusive systems can be captured to a given precision $\ensuremath{\epsilon}$ with an amount of memory scaling as $exp[\mathcal{O}(log{(\ensuremath{\epsilon})}^{2})]$, significantly better than the naive estimate of memory $exp[\mathcal{O}(\mathrm{poly}({\ensuremath{\epsilon}}^{\ensuremath{-}1}))]$ required by more brute-force methods.

Multi-Channel LED Luminaires: An Object-Oriented Approach for Retail Lighting Based on the SOR Framework

In this paper, a method to find the optimum spectrum for the illumination of objects in a retail environment is presented. A variety of familiar objects are illuminated with a number of illuminants of a predefined Correlated Color Temperature (CCT) of 3000 K, strategically selected from the entire range of metamers, which can be generated by the multi-channel luminaire under test. The solution space has been derived by solving basic colorimetric equations using a brute force method. In a paired comparison experiment, observers had to select the most “attractive” appearance for the presented objects. The results illustrate that objects may indeed appear more attractive for a statistically meaningful number of observers under a particular lighting condition. Assuming attractiveness of an object as a stimulus in the SOR framework, this approach facilitates the generation and the selection of the “optimum” spectrum based on the goals of the stakeholders in retail lighting applications.

Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms

SUMMARYRoot phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three‐dimensional functional–structural plant model, SimRoot, to the Borg Multi‐Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade‐offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non‐mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi‐objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.

Implementation of combined steganography and cryptography vigenere cipher, caesar cipher and converting periodic tables for securing secret message

The development of the communication technology era has made it easier in the process of exchanging information from various parties. However, not all of the information provided can be used by many parties. Sometimes the information is intended for partial purposes only or is confidential. So, it is necessary to process information security. Several ways can be used such as cryptography. This method is used to encrypt messages or information. Methods that can be used in cryptography include vigenere cipher and caesar cipher methods. Both of these methods use the technique of replacing each original character with a character or letter. Substitution is based on keywords or character position shift command. However, this method is quite easy to solve by using the Brute Force method and the frequency of letters that often appear. Based on this problem, it is necessary to modify or combine it so that it is more difficult to crack. This research describes the combination of the vigenere cipher and the caesar cipher methods. The two methods were then converted to the names of the chemical elements in the periodic table. Furthermore, the steganography process is carried out as a place to insert messages or information. From this process, it is expected that the encryption results will be more difficult to crack

A real-time defect detection in printed circuit boards applying deep learning

Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustness

Sherlock: A Multi-Objective Design Space Exploration Framework

Design space exploration (DSE) provides intelligent methods to tune the large number of optimization parameters present in modern FPGA high-level synthesis tools. High-level synthesis parameter tuning is a time-consuming process due to lengthy hardware compilation times—synthesizing an FPGA design can take tens of hours. DSE helps find an optimal solution faster than brute-force methods without relying on designer intuition to achieve high-quality results. Sherlock is a DSE framework that can handle multiple conflicting optimization objectives and aggressively focuses on finding Pareto-optimal solutions. Sherlock integrates a model selection process to choose the regression model that helps reach the optimal solution faster. Sherlock designs a strategy based around the multi-armed bandit problem, opting to balance exploration and exploitation based on the learned and expected results. Sherlock can decrease the importance of models that do not provide correct estimates, reaching the optimal design faster. Sherlock is capable of tailoring its choice of regression models to the problem at hand, leading to a model that best reflects the application design space. We have tested the framework on a large dataset of FPGA design problems and found that Sherlock converges toward the set of optimal designs faster than similar frameworks.

Full-duplex routing with low-complexity sequential-decision for throughput enhancement in dynamic access networks

Integrating the Full-Duplex (FD) communication technology in multi-hop Cognitive Radio Networks (CRNs) can significantly enhance routing decisions such that overall network performance is improved. Unfortunately, when employing the FD technology in multi-hop routing, the channel assignment becomes very challenging due to the possible simultaneous transmissions/reception. Hence, careful low-complexity channel assignment over the various hops along a path is needed to avoid hop-to-hop interference and enhance network performance. In this paper, we propose a low-complexity sequential-decision routing protocol, referred to as Sequential FD (SFD)-aware protocol. The SFD protocol identifies a set of possible routes between any source–destination communicating pair. For each discovered route, SFD successively assigns channels for each hop along the router such that the maximum route capacity is achieved. Then, SFD selects the routes with the highest capacity. Unlike most of existing FD-CRN channel assignment/routing protocols, which require optimization procedures with NP-hard time complexity with the number of channels and hops, the time-complexity of the proposed SFD protocol is polynomial. Compared to the optimal FD-aware routing (obtained using the brute-force search method) and maximum per-hop rate FD-unaware routing protocols, the simulation results confirm the effectiveness of our proposed protocol and its superiority over the existing FD-unaware baseline routing protocol in terms of the end-to-end network throughput. The results also confirm that the SFD protocol provides solutions within 5% of the optimal ones.

Determination of coordinates of the earthquake hypocenter with a known structure of the earth's crust

Objective. The aim of the study is to develop methods for determining the coordinates of the hypocenter of an earthquake with a known structure of the earth's crust. Method. To solve the problem, the brute force method, the confidence interval, the comparative method and the enumeration of possible options on the grid are used. Result. A method has been developed for determining the coordinates of an earthquake source with a known structure of the earth's crust using points of various figures of the n-th order as geometric locations in calculations. Conclusion. The proposed method, with the known structure of the earth's crust, makes it possible to use all known methods that require information on the travel time differences of seismic waves to spaced seismic sensors and the difference in travel times of longitudinal and transverse seismic waves to the seismic sensor for calculating the coordinates of the earthquake hypocenter. The accuracy of determining the coordinates of the earthquake hypocenter depends on the quality of determining the structure of the earth's crust on which the seismic event was observed.

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

A methodology for automatic simulation-based testing of control systems for autonomous vessels is proposed. The work is motivated by the need for increased test coverage and formalism in the verification efforts. It aims to achieve this by formulating requirements in the formal logic Signal Temporal Logic (STL). This enables automatic evaluation of simulations against requirements using the STL robustness metric, resulting in a robustness score for requirements satisfaction. Furthermore, the proposed method uses a Gaussian Process (GP) model for estimating robustness scores including levels of uncertainty for untested cases. The GP model is updated by running simulations and observing the resulting robustness, and its estimates are used to automatically guide the test case selection toward cases with low robustness or high uncertainty. The main scientific contribution is the development of an automatic testing method which incrementally runs new simulations until the entire parameter space of the case is covered to the desired confidence level, or until a case which falsifies the requirement is identified. The methodology is demonstrated through a case study, where the test object is a Collision Avoidance (CA) system for a small high-speed vessel. STL requirements for safety distance, mission compliance, and COLREG compliance are developed. The proposed method shows promise, by both achieving verification in feasible time and identifying falsifying behaviors which would be difficult to detect manually or using brute-force methods. An additional contribution of this work is a formalization of COLREG using temporal logic, which appears to be an interesting direction for future work.

Heuristic search for multi‐distance measuring equipment station selection

To support alternative positioning, navigation and timing (A-PNT), multi-DME with better geometry than distance measuring equipment (DME)/DME can be used to improve the performance. Different from the existing station selection methods that have been proposed for DME/DME, a station selection method for multi-DME should be considered in order to prevent unnecessary emission of navigation signals because DME receivers have limited number of channels and constrained real-time processing power, and also to limit DME to GNSS L5/E5a interference. A heuristic algorithm is constructed to determine the quasi-optimal selection of DME stations based on geometrical and computational cost restrictions, which is different from a brute-force method checking all possible subsets and selecting the best one. A station selection method for multi-DME is proposed based on the heuristic search. Meanwhile, the degradation of GNSS L5/E5a signal processing that is caused by DME signals is discussed. An advanced station selection method where the DME to GNSS signal interference is taken into account is also proposed. The experimental results show that the methods based on heuristic search can effectively select the quasi-optimal subset of DME stations with less computational expense.

Olympiads in Informatics in Kyrgyzstan

This report provides description of the Multi-stage National Olympiad along with other inner competitions in informatics, the online platform to conduct competitions in informatics in Kyrgyzstan, the participation in IOI and in other international olympiads in informatics. This report also presents the principles and ways to create tasks (naturalness; presentation of real processes; reference to known objects; non-Euclidean spaces; “brute force method is either inapplicable or gives too overestimate of complexity”) and examples of tasks of various levels with classification.

On the application of the LET-model for the approximation of core relative phase permeabilities

The article deals with the problem of adequate interpretation of core relative phase permeabilities and the use of the LET-model in application to this problem. Some existing analytical dependencies for the interpretation of laboratory studies of the OFP are briefly analyzed: the Corey model, the Sigmund and McCaffery model, the Chierici model, the LET-model. The advantage of the LET-model and the high evaluation of this model in independent studies of the authors are described. In the process of preliminary analysis, the authors of the article revealed that the non-trivial issue of determining the adjustable coefficients of the model using mathematical methods was not sufficiently covered, which became the central object of the work. Further, the application of the LET-model is formalized and the methodology for determining the adjustable coefficients of the model (L, E and T) is described. The task of selecting model parameters is interpreted as a multidimensional optimization problem. Also, a class of nonlinear least squares methods was defined to solve the problem. Several common numerical optimization methods for selecting the coefficients of the model are considered: the brute-force search method, the Levenberg-Marquardt algorithm method, the trust region method. Computational experiments were carried out to assess their applicability and comparative analysis of optimization algorithms. Based on the results of the experiments, it was concluded that the method of confidence domains is suitable for practical application, since they converge to the optimal solution quickly enough and demonstrates high accuracy in the selection of coefficients L, E, T. The final part of the article demonstrates the difference between the LET-model and the basic Corey model when approximating core relative phase permeabilities.

Notice of Violation of IEEE Publication Principles: VVC/H.266 Intra Mode QTMT Based CU Partition Using CNN

Notice of Violation of IEEE Publication Principles <br><br>“VVC/H. 266 Intra Mode QTMT Based CU Partition Using CNN” <br>by Sameena Javaid, Safdar Rizvi, Muhammad Talha Ubaid, Abdullah Tariq <br>in IEEE Access, April 2022 <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper copied content from the article below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “DeepQTMT: A Deep Learning Approach for Fast QTMT-Based CU Partition of Intra-Mode VVC” <br> by Tianyi Li, Mai Xu, Runzhi Tang, Ying Chen, Qunliang Xing <br>in IEEE Transactions on Image Processing, Vol 30, 2021 <br><br> <br/> The latest standard for video coding is versatile video coding (VVC) / H.266 which is developed by the joint video exploration team (JVET). Its coding structure is a multi-type tree (MTT) structure, which consists of two types of trees that are Ternary Tree (TT) and Binary Tree (BT). Due to the use of brute force quest for residual rate-distortion the quad-tree and multi-type tree (QTMT) structure of the coding unit (CU) split and contributes over 98% of the encoding time. This structure is efficient in coding, however, increases computational complexity. The current paper proposes a deep learning technique to predict the QTMT based CU split rather than just the brute-force QTMT method to substantially speed up the time of the encoding process for VVC/H.266 intra mode. In the first phase, we developed an extensive database containing ample CU splitting patterns and various streaming videos. In the second phase, we suggest a multi-level exit CNN (MLE-CNN) model with a redundancy removal mechanism at different levels to determine the CU partition. In the third phase, for the training of MLECNN model we have established the adaptive loss function and analyzing the both unknown number of partition modes and the focus on RD cost minimization. Finally, a variable threshold decision system is established to achieve the targeted low complexity and RD performance. Ultimately experimental findings show that VVC/H.266 encoding time has reduced to 69.11% from 47.91% with insignificant bjontegaard delta bit rate (BDBR) to 2.919% from 1.023% which performs better than the existing futuristic and modern approaches.

Procedure for regularization of bilinear optimal control problems based on a finite-dimensional model

An optimization problem of a linear system of ordinary differential equations on a set of piecewise continuous scalar controls with two-sided restrictions is considered. The cost functional contains the bilinear part (control, state) and a control square with a parameter, which plays the role of a regularization term. An approximate solution of the optimal control problem is carried out on a subset of piecewise constant controls with a non-uniform grid of possible switching points. As a result of the proposed parametrization, reduction to the finite-dimensional problem of quadratic programming was carried out with the parameter in the objective function and the simplest restrictions. In the case of a strictly convex objective function, the finite-dimensional problem can be solved in a finite number of iterations by the method of special points. For strictly concave objective functions, the corresponding problem is solved by simple or specialized brute force methods. In an arbitrary case, parameter conditions and switching points are found at which the objective function becomes convex or concave. At the same time, the corresponding problems of mathematical programming allow a global solution in a finite number of iterations. Thus, the proposed approach allows to approximate the original non-convex variation problem with a finite-dimensional model that allows to find a global solution in a finite number of iterations.