Proposed Search Algorithm Research Articles

Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image

Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.

An Hybrid Local Search for the Direct Aperture Optimisation Problem

Radiotherapy is a cancer treatment that uses high levels of radiation to destroy cancerous cells and shrink tumours while minimising harm to surrounding organs at risk (OARs). One of the techniques used in radiotherapy is Intensity Modulated Radiation Therapy (IMRT). Usually, the IMRT problem is approached sequentially, that is, we first need to determine the set of beam angles from which radiation will be delivered. Then, the radiation intensities for each selected beam angle are computed. Finally, the sequence of aperture shapes needed to deliver the computed treatment plan is generated. Unfortunately, the treatment plans generated by this approach have many apertures, which leads to longer treatment times. In contrast, the Direct Aperture Optimisation (DAO) problem considers constraints associated with the number of deliverable aperture shapes and physical constraints of the machine during the optimisation process of the intensities. The DAO approach generates, in general, better treatments with fewer apertures for IMRT. This is important because fewer apertures usually means shorter delivery times. In this work, we propose an hybrid local search strategy with mathematical programming to efficiently solve the DAO problem. We apply our proposed local search algorithm to a set of prostate cases, obtaining very competitive results.

Towards accelerating model parallelism in distributed deep learning systems.

Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing among GPUs with communication overhead. Specifically, we identify two key issues to make the parallelism being inefficient and inaccurate; an efficient pipelining technique is crucial to maximize GPU utilization and normalizations in deep neural networks may affect the performance due to different statistics sharing of mini-batch. In this work, we address these issues by investigating efficient pipelining for model parallelism and effective normalizations in model / data parallelisms when training a model with large mini-batch in multiple GPUs so that the model performance in accuracy can not be compromised. Firstly, we propose a novel method to search for an optimal micro-batch size considering the number of GPUs and memory size for model parallelism. For efficient pipelining, mini-batch is usually divided into smaller batches (called micro-batch). To maximize the utilization of GPU computing resources, training should be performed with the optimal micro-batch size. Our proposed micro-batch size search algorithm achieved increased image throughput by up to 12% and improved trainable mini-batch size by 25% as compared to the conventional model parallelism method. Secondly, we investigate normalizations in distributed deep learning training for different parallelisms. Our experiments using different normalization methods suggested that the performance with batch normalization can be improved by sharing the batch information among GPUs when performing data parallelism. It was also confirmed that group normalization helped minimizing accuracy degradation when performing model parallelism with pipelining and yielded consistent accuracies for diverse mini-batch sizes.

An Efficient PSO-Based Algorithm for Finding Maximal Exact Match in Large DNA Sequences

With the appearance of complete mammalian genomes, comparative approaches have experienced a recent upsurge. Searching maximal exact match is among the most used tasks in sequence searching within a larger DNA sequence or database. Many exact algorithms have been designed to deal with this problem. The best improvements made by these algorithms have led to a time and space complexity of O(n) and they remain practically less effective for large sequences. Heuristic methods will therefore be a good alternative to implement. In this work, we present an efficient heuristic algorithm based on PSO metaheuristic to deal with the problem of searching the maximal exact match of small searched sequences in large ones. The time and space complexity of the designed algorithm is of O(1). The experimental results showed the efficiency of the proposed search algorithm for finding maximal exact match in large sequences when compared with two other sequence searching algorithms.

Efficient keyword search on graph data for finding diverse and relevant answers

PurposeThis paper studies a keyword search over graph-structured data used in various fields such as semantic web, linked open data and social networks. This study aims to propose an efficient keyword search algorithm on graph data to find top-k answers that are most relevant to the query and have diverse content nodes for the input keywords.Design/methodology/approachBased on an aggregative measure of diversity of an answer set, this study proposes an approach to searching the top-k diverse answers to a query on graph data, which finds a set of most relevant answer trees whose average dissimilarity should be no lower than a given threshold. This study defines a diversity constraint that must be satisfied for a subset of answer trees to be included in the solution. Then, an enumeration algorithm and a heuristic search algorithm are proposed to find an optimal solution efficiently based on the diversity constraint and an A* heuristic. This study also provides strategies for improving the performance of the heuristic search method.FindingsThe results of experiments using a real data set demonstrate that the proposed search algorithm can find top-k diverse and relevant answers to a query on large-scale graph data efficiently and outperforms the previous methods.Originality/valueThis study proposes a new keyword search method for graph data that finds an optimal solution with diverse and relevant answers to the query. It can provide users with query results that satisfy their various information needs on large graph data.

Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor environments. The goal of the problem is to maximize the detection probability within the minimum search time. We propose a two-stage strategy to achieve this goal. In the first stage, a greedy approach is used to define a set of grid cells with the aim of maximizing the detection probability. In the second stage, an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is minimized. Simulation results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.

Expanding the duopoly Stackelberg game with marginal costs into a multipoly game with lowering the burden of mathematical calculations: a numerical analysis

In game theory, the classic Stackelberg game is a dynamic model of a leader and a follower firm contending in a market. The time evolution with the marginal cost and the dynamical traits of the duopoly and tripoly Stackelberg game can be acquired in the literature. The backward induction procedure, an analytical technique for attaining the Nash equilibrium, becomes grueling as the number of followers augments. To conquer this matter and untangle a multipoly Stackelberg game, this paper designs a numerical process based on seeking the parameter space to gain the maximum total profit. Although, due to the curse of dimensionality, a comprehensive quest in the parameter space fails as the number of followers extends. Therefore, the proposed search algorithm explores the parameter space sequentially to detect the optimum values that assure the Nash equilibrium. The significant concept of this iterative procedure is optimizing the parameters sequentially and utilizing the derived parameters for initializing the subsequent stride. The consequences of the developed numerical strategy represent that the proposed mechanism can conduce to a reasonable and even more acceptable total profit than the analytical attitude. The leader scheduled vintage is invariably greater or similar to the followers so that the leader can inevitably produce more goods and make the maximum profit in the market. The proposed algorithm can conduct a reasonable profit compared to the computational cost. In addition, solving the multipoly Stackelberg game with more than three follower firms and one leader becomes conceivable.

Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme

In order to enhance the anti-submarine capability of multi-unmanned aerial vehicles (multi-UAVs) in the unknown sea environment and improve the search efficiency, in this paper, we propose a rule-inspired-multi-ant colony (RI-MAC)-based UAV cooperative search algorithm. First, a special sea area anti-submarine search model is established, including an association rule-driven target probability map (TPM) model, a UAV kinematics model, and a sensor model. The novel model has the characteristics of rule linkage, which effectively improves the accuracy of target detection probability in unknown environments. Secondly, according to the established search model, a multi-objective utility function based on association rules is derived. In order to solve the problem of multi-objective optimization, an RI-MAC algorithm based on association rules is proposed, and a pheromone update method using threat avoidance is designed to optimize the search path of multi-UAVs. Finally, a simulation experiment is conducted to verify the effectiveness and superiority of the proposed search algorithm.

Experimental study on the quantum search algorithm over structured datasets using IBMQ experience

In this work, a quantum search algorithm over structured datasets is proposed. Subsequently, the algorithm is executed on a real chip quantum computer developed by IBM Quantum experience (IBMQ). QISKit, the software platform developed by IBM, is used for the implementation of this algorithm. Quantum interference, quantum superposition, and π phase shift of the quantum state are applied in the proposed search algorithm. It performs a π phase shift on the initial state and conducts a state elimination and amplitude amplification process to reach the 'search key' or 'solution key' from the given structured dataset. The proposed quantum algorithm is executed using the QISKit SDK local backend ‘local_qasm_simulator’, real chip 'ibmq_16_melbourne', 'ibmq_belem' and 'ibmqx4′ IBMQ. The results suggest that the real chipibmq_16_melbourne is more quantum error- or noise-prone than ibmq_belem and ibmqx4. The correctness, validation, and application using the proposed algorithm are demonstrated. The algorithm is very promising in terms of low quantum cost and fewer gate requirements. This implies that the proposed quantum search algorithm may be a compelling and pertinent choice in the Noisy Intermediate-Scale Quantum (NISQ) technology era.

The partition representation of enzymatic reaction networks and its application for searching bi-stable reaction systems.

The signal transduction system, which is known as a regulatory mechanism for biochemical reaction systems in the cell, has been the subject of intensive research in recent years, and its design methods have become necessary from the viewpoint of synthetic biology. We proposed the partition representation of enzymatic reaction networks consisting of post-translational modification reactions such as phosphorylation, which is an important basic component of signal transduction systems, and attempted to find enzymatic reaction networks with bistability to demonstrate the effectiveness of the proposed representation method. The partition modifiers can be naturally introduced into the partition representation of enzymatic reaction networks when applied to search. By randomly applying the partition modifiers as appropriate, we searched for bistable and resettable enzymatic reaction networks consisting of four post-translational modification reactions. The proposed search algorithm worked well and we were able to find various bistable enzymatic reaction networks, including a typical bistable enzymatic reaction network with positive auto-feedbacks and mutually negative regulations. Since the search algorithm is divided into an evaluation function specific to the characteristics of the enzymatic reaction network to be searched and an independent algorithm part, it may be applied to search for dynamic properties such as biochemical adaptation, the ability to reset the biochemical state after responding to a stimulus, by replacing the evaluation function with one for other characteristics.

Unambiguous BPSK-like CSC Method for Galileo Acquisition

Galileo will be Europe’s own Global Navigation Satellite System (GNSS), which is aiming to provide highly accurate and guaranteed positioning services. Galileo E1 system has a code period of 4ms which is quadruple that of GPS C/A code. In other words, due to the large number of hypotheses in code phase at acquisition stage, a longer searching time or more hardware resource is required. It is difficult to acquire Galileo signal because of longer code length and the multiple peaks of autocorrelation function of BOC modulation. In this paper, the cyclically shift-and-combine (CSC) and BPSK-like architectures are employed to resolve the unambiguous acquisition for BOC modulation and acquires these satellite signals with hardware complexity reduction. The concept of CSC code is to modify the code structure and shorten the code period such that the acquisition burden can be decreased. Simulation results show that our proposed search algorithm can provide better performances in terms of low hardware complexity for acquiring these satellite signals and detection probability at the low value of CNR.

Autofocusing Optimal Search Algorithm for a Telescope System

Focus accuracy affects the quality of the astronomical observations. Auto-focusing is necessary for imaging systems designed for astronomical observations. The automatic focus system searches for the best focus position by using a proposed search algorithm. The search algorithm uses the image’s focus levels as its objective function in the search process. This paper aims to study the performance of several search algorithms to select a suitable one. The proper search algorithm will be used to develop an automatic focus system for Kottamia Astronomical Observatory (KAO). The optimal search algorithm is selected by applying several search algorithms into five sequences of star-clusters observations. Then, their performance is evaluated based on two criteria, which are accuracy and number of steps. The experimental results show that the Binary search is the optimal search algorithm.

A fast fracture plane orientation search algorithm for Puck’s 3D IFF criterion for UD composites

This paper presents a new approach for the search of fracture plane orientation of the Puck’s inter-fibre failure (IFF) failure criterion. The application of Puck’s 3D IFF criterion in finite element codes has faced some limitations due to the computational effort required for the fracture plane search. For a 3D state of stress, this task has to be accomplished by using a numerical method that includes a great deal of iterations, and normally is very time-consuming. The challenge is therefore to construct algorithms that present reliable results for every stress case and at the same time minimize the number of iterations to reduce the computational time. In this work, a new reliable and more efficient algorithm for the fracture plane angle search is proposed. A total of 5 × 104 random stress states are analyzed using the proposed search algorithm. The accuracy and computational cost of the results are then compared with several algorithms available in the literature. Moreover, the new fracture angle search algorithm is implemented in an ABAQUS/Explicit user-defined material model (VUMAT) which considers the progressive failure of the open hole tensile composite specimens. The model is accurate in replicating the mechanical response and the matrix damage in the specimen.

Robust radioactive sources research method using possibility particle filter

Of growing concern for the security of many nations are numerous incidents of lost or stolen radioactive sources or materials. The detection of and search for these abnormal radioactive sources plays an important role in monitoring nuclear safety and disposal of nuclear waste. In this paper, a method for autonomously searching for radioactive sources in a flat open rectangular-shaped field through mobile platforms was proposed. In this method, by using the possibility particle filter, the search for radioactive sources was realized according to a series of radiation information measured by the mobile platform carrying a Geiger–Müller counter. According to the inverse square law and the radiation counting governed by Poisson distribution, a radioactive source localization model was constructed. Then, a mobile platform controlled by an information entropy strategy constantly moved within the search area and detected radiation at specific points. The possibility filter algorithm, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source parameters. The performance of the proposed search algorithm, including a comparison with a standard particle filter algorithm, is studied by simulations. The simulation experiment proves that the possibility particle filter algorithm has good robustness. The successful application of the experimental dataset collected in the simulations verifies the measurement model and theoretical consideration.

SP‐search‐based path planning algorithm for mobile robots using quintic trigonometric Bézier curves

AbstractPath planning is an essential step for mobile robots. This paper presents a novel shape parameter search‐ (SP‐Search) based path planning algorithm for mobile robots using quintic trigonometric Bézier curve and its two shape parameters. The proposed search algorithm recursively divides the shape parameter space into a grid and uses a multi‐objective function to determine the shape parameter values. The multi‐objective function is expressed by the sum of four terms multiplied by coefficients: the integral of the squared jerk, the integral of the squared curvature, the mean of minimum distances between the predefined skeleton and quintic trigonometric Bézier path, and the arclength. Optimal shape parameters can be determined according to the required cases by the user using four coefficient values. Experimental results illustrate the feasibility of the proposed SP‐Search algorithm. Also, we have developed another intuitive algorithm, the Brute Force Grid Search algorithm, which is the brute force approach for shape parameter search to compare the performance of the proposed SP‐search algorithm for path planning problems as a baseline. The experimental results show that the SP‐Search algorithm finds better shape parameter values with a considerably small number of searched points.

Optimal Feature Set Size in Random Forest Regression

One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. Lastly, we developed a search algorithm for estimating the best feature set size in RF regression. We showed that the proposed search algorithm can provide improvements over other choices, such as using the default size specified in the randomForest R package and using the common grid search method.

Multiple-Iteration Search Sensorless Control for Linear Motor in Vehicle Regenerative Suspension

This article presents a sensorless control for the permanent-magnet linear synchronous motor (PMLSM) under the energy-feeding mode in vehicle regenerative suspension. The proposed method is based on a multiple-iterative search algorithm to obtain a limited quantity of mover position signals (electrical angle) precisely, which are utilized to observe the d-axis back electromotive force (EMF). Then, the optimal mover position signal selected from the estimated mover position is restrained by the cost function applied in the multiple-iteration search algorithm. The accuracy of the mover position signal increases geometrically in the effect of an increase in the iterations. The proposed search algorithm is superior to the existing search algorithm in the terms of the computational burden. Different from the conventional phase-locked loop (PLL), the new strategy removes the need for a proportional-integral controller. The proposed control method and the conventional control method are experimentally tested under different conditions. Results prove that the proposed PLL has the effectiveness to realize the estimated parameters corresponding to the actual parameters.

A novel topology-guided attack and its countermeasure towards secure logic locking

The outsourcing of the design and manufacturing of integrated circuits (ICs) in the current horizontal semiconductor integration flow has posed various security threats due to the presence of untrusted entities, such as overproduction of ICs, sale of out-of-specification/rejected ICs, and piracy of Intellectual Properties (IPs). Consequently, logic locking emerged as one of the prominent design for trust techniques. Unfortunately, these locking techniques are now inclined to achieve complete Boolean satisfiability (SAT) resiliency after the seminal work published in Subramanyan et al. (in: International Symposium on Hardware 907 Oriented Security and Trust, pp 137–143, 2015). In this paper, we propose a novel oracle-less attack that is based on the topological analysis of the locked netlist even though it is SAT-resilient. The attack relies on identifying and constructing unit functions with a hypothesis key to be searched in the entire netlist to find its replica. The proposed graph search algorithm efficiently finds the duplicate functions in the netlist, making it a self-referencing attack. This proposed attack is extremely efficient and can determine the secret key within a few minutes. We have also proposed a countermeasure to make the circuit resilient against this topology-guided attack to progress toward a secure logic locking technique.

An integrated algorithm for solving multi-customer joint replenishment problem with districting consideration

This paper studies a multi-customer joint replenishment problem with districting consideration (MJRPDC) which is of particular importance to a company that outsources its transportation and delivery operations to a third-party logistics (3PL) service provider. To solve the problem, we first propose an innovative search algorithm for solving the traditional multi-customer joint replenishment problem in a given zone. Then we design a GA-based framework to handle the corresponding districting problem based on the performance of each district evaluated by using the proposed search algorithm. The proposed methodologies are demonstrated by using an example of solving MJRPDC for a bank.

Low-Cost Stochastic Number Generators for Stochastic Computing

Stochastic unary computing provides low-area circuits. However, the required area consuming stochastic number generators (SNGs) in these circuits can diminish their overall gain in area, particularly if several SNGs are required. We propose area-efficient SNGs by sharing the permuted output of one linear feedback shift register (LFSR) among several SNGs. With no hardware overhead, the proposed architecture generates stochastic bit streams with minimum stochastic computing correlation (SCC). Compared to the circular shifting approach presented in prior work, our approach produces stochastic bit streams with 67% less average SCC when a 10-bit LFSR is shared between two SNGs. To generalize our approach, we propose an algorithm to find a set of $m$ permutations ( $n > m > 2$ ) with a minimum pairwise SCC, for an $n$ -bit LFSR. The search space for finding permutations with an exact minimum SCC grows rapidly when $n$ increases and it is intractable to perform a search algorithm using accurately calculated pairwise SCC values, for $n > 9$ . We propose a similarity function that can be used in the proposed search algorithm to quickly find a set of permutations with SCC values close to the minimum one. We evaluate our approach for several applications. The results show that, compared to prior work, it achieves lower mean-squared error (MSE) with the same (or even lower) area. Additionally, based on simulation results, we show that replacing the comparator component of an SNG circuit with a weighted binary generator can reduce SCC.