Search Speed Research Articles

Open Innovation: How Is the Search for Innovation Affected by Alliance Portfolio Diversity?

The relationship between the alliance portfolio diversity and the search for innovation is explored based on resource view in open innovation. Three elements of the alliance portfolio diversity—industry, organizational, and national diversity—are theorized to affect the result of the search for innovation. The hypothesis are tested by using regressions analysis method with the date collected from 550 new generation of information technology companies in Chengdu Tianfu Software Zone and Wuxi (Taihu) International Technology Zone. The results show that the alliance portfolio diversity has a significant positive effect on innovative search quality. We also find that, the organizational diversity has no impact on innovative search speed. Conclusions is expected to provide inspiration to the enterprise, which are integrating resources to achieve break through innovation bottleneck and technological upgrading.

Transceiver Decoupling of Multivariate Symmetric Hybrid Precoding Based on 5G

Mobile Internet will promote the continuous change of human interaction, leading to an increase in mobile traffic, so the demand for network bandwidth and data volume is rising rapidly, which is also one of the problems that 5G needs to solve. The mobile communication network of the railway system has the characteristics of high-speed user mobility, large-scale group mobility of users, high certainty of user mobile lines, and high QoS requirements for dispatching information. In order to improve the transmission reliability requirements of the railway system for wireless communication, a quick search method algorithm based on GMCS model to encode the number of each subinterval is proposed. Hybrid precoding is designed according to multivariate symmetry rules. The target beam is designed according to the GMCS model, and the hierarchical training beam is designed to minimize the mean square error between the training beam and the target beam as the objective function. Then, the fast search model based on beam overlap is extended to NLoS to solve the problem of misjudgment caused by multipath. In the simulation experiment, it proves that the search success rate of the research in this paper is 10% higher than that of the traditional algorithm. It improves the search speed and has obvious advantages in complexity. It can provide a dynamic reliable conversion mechanism for the railway communication environment, reduce the transmission power of the base station, and optimize the actual effect of uplink and downlink service requirements.

Trinity: Neural Network Adaptive Distributed Parallel Training Method Based on Reinforcement Learning

Deep learning, with increasingly large datasets and complex neural networks, is widely used in computer vision and natural language processing. A resulting trend is to split and train large-scale neural network models across multiple devices in parallel, known as parallel model training. Existing parallel methods are mainly based on expert design, which is inefficient and requires specialized knowledge. Although automatically implemented parallel methods have been proposed to solve these problems, these methods only consider a single optimization aspect of run time. In this paper, we present Trinity, an adaptive distributed parallel training method based on reinforcement learning, to automate the search and tuning of parallel strategies. We build a multidimensional performance evaluation model and use proximal policy optimization to co-optimize multiple optimization aspects. Our experiment used the CIFAR10 and PTB datasets based on InceptionV3, NMT, NASNet and PNASNet models. Compared with Google’s Hierarchical method, Trinity achieves up to 5% reductions in runtime, communication, and memory overhead, and up to a 40% increase in parallel strategy search speeds.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

Capacity Configuration Method of Hybrid Energy Storage Participating in AGC Based on Improved Meta-Model Optimization Algorithm

To improve the performance and economy of the hybrid energy storage system (HESS) coordinating thermal generators to participate in automatic generation control (AGC), a HESS bi-layer capacity configuration model that considers the control strategy and net benefits of HESS is proposed. In addition, an improved mode-pursuing sampling (MPS) optimization algorithm based on meta-model is presented to improve the accuracy of model solving. In the lower layer, to improve the performance of HESS participating in AGC, a model predictive control (MPC) strategy is presented to distribute HESS power reasonably. Based on this, the upper layer develops a life-cycle net benefit model of HESS participating in AGC to improve its economy. The bi-layer model realizes iterative optimization of HESS capacity and operation through parameter transmission. Furthermore, to improve the solution accuracy of the bi-layer model, the convergence speed of the MPS algorithm is improved, so that the global search and local convergence speed can be taken into account. The case study results show that the bi-layer model can comprehensively consider the interaction between the economy and operating strategy of HESS. The proposed MPC strategy has better frequency regulation performance and the improved MPS algorithm has better solution performance.

Obstacle avoidance path scheme of snake robot based on bidirectional fast expanding random tree algorithm

With the continuous innovation and development of related technologies in the field of robotics, various robots have emerged in large numbers, the application scenarios of robots are becoming more and more complex, and the requirements for robot technology are also getting higher and higher. As an indispensable part of robotics research, path planning technology has very important research and application value. As a new movable snake-shaped robot, the snake-shaped robot has always been a research hotspot in the field of snake-shaped robots due to its multi-gait movement ability and strong environmental adaptability. Among the various motion forms of the snake-shaped robot, the winding motion has the highest efficiency, and the snake-shaped robot in this motion form can also assist itself in its movement by contact with obstacles. However, there are few researches on the obstacle-assisted motion of snake-like robots, and the algorithm for solving collision dynamics is relatively simple. Therefore, it is necessary to conduct in-depth research on obstacle-assisted motion snake-like robots. The trajectory planning algorithm of the multi-NAO snake robot is studied. First, it analyzes the basic principles and implementation steps of the RRT algorithm and evaluates its advantages and disadvantages. On this basis, an improved fast-expanding random algorithm is proposed for the shortcomings of the RRT algorithm. Combining the advantages of the global search of the RRT algorithm, it is necessary to introduce an appropriate local search algorithm. Under the premise of ensuring that the path can be generated, a certain algorithm is added to improve the smoothness of the path, in order to reduce the turning time of the snake-like robot when it walks, and improve the search efficiency. Aiming at the randomness of RRT when generating random tree nodes, other algorithms are purposefully introduced to enable it to grow toward the target point. Path planning simulations verify that the improved two-way rapid expansion random tree algorithm has significantly improved search speed and search efficiency compared with Basic-RRT and Bi-RRT algorithms, with shorter average planning time and higher success rate, the generated path is smoother. Improved algorithm can not only avoid static global obstacles in a dynamic environment, but also efficiently avoid sudden dynamic obstacles. The real-time dynamic obstacle avoidance experiments based on the FANUC snake-shaped robot guiding the anthropomorphic obstacles to move and rotate in translation and the snake-shaped robot dynamically avoiding real human arms verify that the proposed algorithm can dynamically avoid regular and irregular moving obstacles online in time. When the number of obstacles is 3, the number of successful plans is 49, and the planning time is 55 ms; when the number of obstacles is 12, the number of successful plans is 40, and the planning time is 88.9 ms. Obviously, the increase in the number of obstacles leads to a decrease in the number of successful planning and an increase in planning time.

Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

AbstractIn recent years, the large amount of heterogeneous data generated by the Internet of Things (IoT) sensors and devices made recording and research tasks much more difficult, and most of the state‐of‐the‐art methods have failed to deal with the new IoT requirements. This article proposes a new efficient method that simplifies data indexing and enhances the quality and velocity of the similarity query search in the IoT environment. In this method, the fog layer was divided into two levels. In the clustering fog level, the incremental density‐based spatial clustering of applications with noise (DBSCAN) algorithm was used to separate collected data into clusters in order to minimize data overlap during in parallel indexes construction. Parallelism was also used, in the indexing fog level to speed up the similarity‐based search process and speed up the similarity‐based search process. The data in each cluster were indexed using our proposed structure called B3CF‐tree (binary tree based on containers at the cloud‐clusters fog computing level). The objects in the leaf nodes of the B3CF‐trees are, finally, stored in the cloud. Using this approach for computing multiple datasets, the retrieve time of the similarity search is significantly reduced. The experimental results showed that the combination of DBSCAN clustering and parallel indexing make the B3CF‐trees outperform the latest real data indexing methods. For example, in terms of quality, the B3CF‐tree has the smallest number of nodes and leaf nodes. In addition, the use of parallelism during kNN search reduced, significantly, the retrieve time of the similarity query search.

Particle swarm optimization gray wolf fusion algorithm for solving the optimal solution of function

In order to improve the local difference problem of wolf swarm algorithm, which is easy to fall into local optimization in solving function optimization, a particle swarm grey wolf fusion algorithm is proposed based on the characteristics of particle swarm algorithm, such as fast search speed, strong ability and easy implementation. Firstly, this paper compares particle swarm optimization and wolf swarm algorithm, and introduces particle swarm optimization gray wolf fusion algorithm by analyzing the root of the problem. The experimental results show that the particle swarm wolf swarm algorithm is feasible, and the search accuracy, stability and the optimal value of the objective function are significantly improved in the solution process.

Path planning of mobile robot based on genetic-ant colony algorithm

Mobile robots are widely used in daily life, and ant colony algorithm is often adopted for its path planning. However, the classic ant colony algorithm has poor search ability and search speed due to the monotony of individuals within the population. In order to solve the problems of local optimal solution, slow convergence speed and low search efficiency of classic ant colony algorithm, a genetic-ant colony algorithm is proposed in this paper. The algorithm parameters are optimized by simulation experiment to determine the best parameter combination. The method is to give a certain range of random pheromone values to the primary ant individuals to enrich the population diversity. In each cycle, m ants are sent for foraging. After each foraging cycle, aiming at the growth of individual pheromone value, the fitness function of genetic algorithm is established to evaluate individual fitness value, and the parental individuals carry out genetic operations such as selection, crossover and variation to produce offspring individuals with the same number and higher pheromone level, the offspring individuals carry out the next cycle of foraging and keep cycling. The population diversity is maintained at a high level, while the increase in the level of individual pheromone speeds up the convergence and operation of the algorithm. The simulation results show that the running time of the improved algorithm is significantly shorter than that of the traditional ant colony algorithm in the 40 * 40 grid map, and the running time of the algorithm is reduced by 21.34%; in the complex grid environment, the frequency of obtaining the optimal path of the algorithm is significantly higher than that of ordinary ant colonies, and the frequency is increased; and the number of iterations when the optimal path is reached for the first time is significantly reduced,–which has verified the effectiveness, stability and superiority of genetic-ant colony algorithm.

Localizing unknown radiation sources by unscented particle filtering based on divide-and-conquer sampling

ABSTRACT Predictive estimation of radioactive sources using Bayesian theory is of great importance in nuclear emergency response. To this aim, we put forward an unscented particle filtering algorithm based on divide-and-conquer sampling. Our method exploits the information acquired by mobile detection robots to search for radioactive sources at unknown locations. Firstly, we assume a circular and extremely thin NaI (TI) scintillator detector, then analyze the geometric relationship between the detector cross-sectional area and the square of the radiation source distance, and finally establish a radiation source search model in accordance with Bayesian method. We also introduce a divide-and-conquer strategy to solve the problems of particle dilution and particle degradation, so as to achieve higher search accuracy and speed. Through simulation experiments, compared with standard particle filter and unscented particle filter, the algorithm can effectively improve particle diversity and successfully search radioactive sources in a shorter time.

Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

Keyword-Covered Group Enlargement Community Search

The goal of community search across attributed graphs is to locate the community that takes both attribute cohensiveness and constrained structure into account. The keyword closeness of subgraph is usually measured by similarity distance. However, existing works focus on how to find a community that has most relevant to the keywords of the query vertex through the similarity score, whereas we pay more attention to a community that can jointly cover keywords and find subgraph with the maximum core. To address this problem, we propose a novel query keyword-covered group enlargement community search [Formula: see text]. Given an initial subgraph and a set of query keywords, the [Formula: see text] search aims to find the community which satisfies the following conditions: (1) it jointly covers all query keywords; (2) it is a subgraph with the maximum core; (3) it is added the minimum vertex set that meets the conditions (1) and (2). We design a baseline enumerateKGEC algorithm ([Formula: see text]), which enumerates all the vertex combinations that cover the remaining query keywords. To further accelerate the search speed, we propose two heuristic algorithms candidate set-based algorithm [Formula: see text] and candidate set and keyword combination algorithm [Formula: see text], which can effectively speed up the search and find a feasible solution. Finally, we evaluate the performance of our algorithms on two real datasets and show effectiveness and efficiency of our algorithms for [Formula: see text] problem.

Hopf bifurcation and Bautin bifurcation in a prey–predator model with prey’s fear cost and variable predator search speed

This paper considers an extended predator–prey model, in which the fear of predators reduces prey reproduction and the search speed of predators depends on prey density. First, our mathematical analysis shows that high levels of fear can stabilize the coexistence steady state, while low levels would result in periodic oscillation. Comparing to the model ignoring fear where supercritical bifurcation occurs, Hopf bifurcation in our model can be both supercritical and subcritical, which leads to bi-stability and two limit cycles. Second, our analysis demonstrates that a relatively small search speed of predators can promote stability of the coexistence steady state, while a large speed would lead to periodic oscillation. Comparing to the model with invariant search speed where Hopf bifurcation takes place, Bautin bifurcation occurs in our model, which results in tri-stability and three limit cycles. While the paradox of enrichment always takes place in the Holling-type II predation model, it does not occur here when the search speed is small. Even when the speed is large, the prey species can adapt by enhancing their fear level and stabilize the system effectively. Third, our analysis shows that enhancing prey’s sensitivity to predation risk or slowing the predator search speed, can stabilize the coexistence steady state, while a low sensitivity and a high speed will lead to periodic oscillation. Numerical simulations confirm and extend our results.

Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint Stability Strategy Optimization.

Based on the nonlinear disturbance observer (NDO), the inversion time-constraint stability strategy (ITCS) is designed to make the deep-sea self-holding intelligent buoy (DSIB) system hovered at an appointed depth within a specified time limit. However, it is very challenging to determine the optimal parameters of an ITCS depth controller. Firstly, a genetic algorithm based on quantum theory (QGA) is proposed to obtain the optimal parameter combination by using the individual expression form of quantum bit and the adjustment strategy of quantum rotary gate. To improve the speed and accuracy of global search in the QGA optimization process, taking the number of odd and even evolutions as the best combination point of the genetic and chaos particle swarm algorithm (GACPSO), an ITCS depth controller based on GACPSO strategy is proposed. Besides, the simulations and hardware-in-the-loop system experiments are conducted to examine the effectiveness and feasibility of the proposed QGA–ITCS and GACPSO–ITCS depth controller. The results show that the proposed GACPSO–ITCS depth controller provides higher stability with smaller steady-state error and less settling time in the depth-control process. The research of the proposed method can provide a stable operation condition for the marine sensors carried by the DSIB.

A Lévy Distribution Based Searching Scheme for the Discrete Targets in Vast Region

This paper investigates the Discrete Targets Search Problem, (DTSP), which aims to quickly search for discrete objects scattered in a vast symmetry region. Different from continuous function extremal value search, the discrete points search cannot make use of the properties of regular functions, such as function analytic, single/multiple extreme, and monotonicity. Thus, in this paper a new search scheme based on Lévy random distribution is investigated. In comparison with the TraditionalCarpet search or Random search based on other distributions, DTSP can provide much faster search speed which is demonstrated by simulation with different scales problems for the selected scenarios. The simulations experiment proves that DTSP is faster for searching for a discrete single target or multiple targets in a wide area. It provides a new method for solving the discrete target search problem.

Morphological combinatorics of folk diminutives in the lullabies of Greek, Serbian and Ukrainian languages

The article focuses on morphological models of combinatorics of diminutive suffixes in lullabies in Modern Greek, Serbian and Ukrainian. A method of computer and targeted sampling of folklore diminutives in lullabies in the studied languages was developed.The "Multi Text Finder" software was adapted for computer and targeted sampling of diminutive suffixes in electronic texts of lullabies in Modern Greek, Serbian and Ukrainian and their search was performed according to the following algorithm: search location (document) was selected; diminutives in each language were loaded to search; words with diminutive suffixes were searched; the automatically selected units that are not the object of the scientific research were sorted out. The advantages of the used computer programme proved to be the following: the ability to search multiple word elements, support of basic document formats (TXT, DOC, RTF, etc.), integration with Windows Explorer, preview of search items, good search speed, user-friendliness.It was established that adding diminutive suffixes to the stem causes combinatorial morphological changes that occur due to the interaction of phonemes because of their distribution. As a result of the interaction of two adjacent phonemes (stem final and diminutive suffix) one of them may feature changes of: qualitative (accommodation, assimilation, dissimilation) or quantitative (simplification, lengthening and contraction) character. The comparative analysis helped to find out that morphological combinatorial changes are almost uncharacteristic of masculine diminutives in Modern Greek and neuter diminutives in Serbian. As for the feminine gender, combinatorial changes occur in all three languages. In Modern Greek the unique characteristic is the addition, which is a combinatorial morphological change, created by joining the feminine suffix -οπούλ-α and the neuter suffix -άκι to the stem.

A hybrid optimization method by incorporating adaptive response strategy for Feedforward neural network

Particle swarm optimisation algorithm (PSO) possesses a strong exploitation capability due to its fast search speed. It, however, suffers from an early convergence leading to its inability to preserve diversity. An improved particle swarm optimiser is proposed based on a constriction factor and Gravitational Search Algorithm to overcome premature convergence. The constriction factor ensures an appropriately controlled transition from exploration into exploitation, leading to an enhanced diversity and appropriate learning rate adjustment throughout the search process. We introduce Gravitational Search Algorithm to enhance the exploratory ability of PSO. An adaptive response strategy is incorporated to activate stagnated particles to curtail the high tendency to get trapped in a local optimum. To verify the efficacy of the improvement strategies, we employ the proposed algorithm in training a Single Layer Feedforward neural network to classify real-world data ranging from binary to multi-class datasets of which our proposed algorithm outperforms the others.

Computational Intelligence and Metaheuristic Techniques for Brain Tumor Detection through IoMT-Enabled MRI Devices

The brain tumor is the 22nd most common cancer worldwide, with 1.8% of new cancers. It is likely the most severe ailment that necessitates early discovery and treatment, and it requires the competence of neurosubject-matter experts and radiologists. Because of their enormous increases in data search and extraction speed and accuracy, as well as individualized treatment suggestions, machine and deep learning techniques are being increasingly commonly applied throughout healthcare industries. The current study depicts the methodologies and procedures used to detect a tumor inside the brain utilizing machine and deep learning techniques. Initially, data were preprocessed using contrast limited adaptive histogram equalization. Then, features were extracted using principal component analysis and independent component analysis (ICA). Next, the image was smoothed using multiple optimization techniques such as firefly and cuckoo search, lion, and bat optimization. Finally, Naïve Bayes and recurrent neural networks were utilized to classify the improved results. According to the findings, the ICA with cuckoo search and Naïve Bayes has the best mean square error rate of 1.02. With 64.81% peak signal-to-noise and 98.61% accuracy, ICA with hybrid optimization and a recurrent neural network (RNN) proved to better than the other algorithms. Furthermore, a Smartphone application is designed to perform quick and decisive actions. It helps neurologists and patients identify the tumor from a brain image in the early stages.

Applications of Big Data in Economic Information Analysis and Decision-Making under the Background of Wireless Communication Networks

Owing to the growing volumes of mobile telecommunications customers, Internet websites, and digital services, there are more and more big data styles and types around the world. With the help of big data technology with high semantic information, this paper focuses on exploring the value and corresponding application of big data in finance. By comparing with the existing methods in terms of search speed and data volume, we can effectively see the effectiveness and superiority of the algorithm proposed in this paper. Furthermore, the algorithm proposed in this paper can provide some reference ideas for the follow-up-related research work.

Improved lazy theta∗ algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle (UAV) in three-dimensional map. Firstly, in order to keep a safe distance between UAV and obstacles, the obstacle grid in the map is expanded. By using the data structure of octree, the octree map is constructed, and the search nodes is significantly reduced. Then, the lazy theta∗ algorithm, including neighbor node search, line-of-sight algorithm and heuristics weight adjustment is improved. In the process of node search, UAV constraint conditions are considered to ensure the planned path is actually flyable. The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes. Heuristic weight adjustment strategy is employed to control the precision and speed of search. Finally, the simulation results show that the improved lazy theta∗ algorithm is suitable for path planning of UAV in complex environment with multi-constraints. The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.