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In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior. [ABSTRACT FROM AUTHOR]

In the order picking process of the warehouse center, considering the rapid increase in the volume of orders arriving at the picking center at the time of the promotional festival, a hybrid operation mode with multiple picking tables is used to meet the picking requirements of the huge number of real-time orders. Therefore, in this paper, a hybrid picking mode is proposed, taking into account both the idle degree of picking stations and their order item centers of gravity, and a new reinforcement learning algorithm embedding mechanism (PRL) with placeholder control is designed to solve the problem of a huge number of real-time item orders arriving at the picking center system on promotional holidays and in inconsistent quantities, and numerical simulations are performed for this algorithm. The experimental results show that the PRL algorithm in hybrid picking mode can handle a huge number of orders simultaneously and improve picking efficiency effectively. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%. [ABSTRACT FROM AUTHOR]

Attitude measurement is an important core technology of vehicle flight. It is of great significance to ensure the vehicle's accurate orbit entry and orbit change, high-performance flight, reliable ground communication, high-precision ground observation, and successful completion of various space missions. Star sensor is the core component to realize an autonomous attitude measurement of the vehicle. Autonomous star map recognition is a key technology in star sensor technology, and it is also the focus and difficulty of research. When the star sensor enters the initial attitude acquisition mode, according to the algorithm, the star sensor can quickly obtain the initial attitude and enter the normal working mode. This paper proposes a star pattern recognition algorithm based on the radial companion-circumferential feature with a noise compensation code to address the low recognition rate caused by position noise in the process of constructing star patterns in the traditional star pattern recognition algorithm based on the radial feature. In order to solve the problem of slow matching search speed, a maximum matching number algorithm has been innovatively adopted, which can improve the search efficiency in the process of star pattern recognition. Thus, the capacity of the star pattern recognition feature library is effectively reduced, and the stability and recognition rate of the improved star pattern recognition algorithm are further improved. The improved star pattern recognition algorithm first establishes radial and circumferential feature vectors based on the bit vector, then adds the noise compensation code according to the companion star position error, then modifies the radial and circumferential feature vectors, and finally calculates the minimum similarity difference between the feature vector of the star pattern observed by the star sensor and the feature vector of the navigation star in the feature library to obtain the unique star pattern recognition result. The identification star database adopts the maximum matching number algorithm, which can improve the search efficiency, reduce the amount of redundant matching, and shorten the matching time. The simulation results show that even in the presence of star position and magnitude noise, the improved star pattern recognition algorithm with radial companion-circumferential feature maintains a high recognition rate of more than 97 percent, demonstrating that the algorithm's robustness is superior to other algorithms. The revised method described in this work outperforms the classic triangle algorithm and the radial feature star pattern recognition algorithm without compensation code in terms of algorithm robustness, recognition success rate, and recognition time. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the subspace-like direction of arrival (DOA) estimation performs poor due to the error of sources number, this paper proposes a new super-resolution DOA estimation algorithm based on the diagonal-symmetric loading (DSL). Specifically, orthogonality principle of the minimum eigenvector of the specific covariance matrix and the source number estimation based on the improved K-means method were adopted to construct the spatial spectrum. Then, by considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters was given and verified by numerical experiment. To evaluate the effectiveness of the proposed algorithm, this paper compared it with the methods of minimum variance distortionless response (MVDR) and new signal subspace processing (NSSP). Experimental results prove that the proposed DSL has higher resolution and better estimation accuracy than the MVDR and NSSP. [ABSTRACT FROM AUTHOR]

This paper presents an efficient outer space branch-and-bound algorithm for globally solving a minimax linear fractional programming problem (MLFP), which has a wide range of applications in data envelopment analysis, engineering optimization, management optimization, and so on. In this algorithm, by introducing auxiliary variables, we first equivalently transform the problem (MLFP) into the problem (EP). By using a new linear relaxation technique, the problem (EP) is reduced to a sequence of linear relaxation problems over the outer space rectangle, which provides the valid lower bound for the optimal value of the problem (EP). Based on the outer space branch-and-bound search and the linear relaxation problem, an outer space branch-and-bound algorithm is constructed for globally solving the problem (MLFP). In addition, the convergence and complexity of the presented algorithm are given. Finally, numerical experimental results demonstrate the feasibility and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Torque ripple is a defect of switched reluctant motors. DITC (direct instantaneous torque control) is harnessed to solve the problem as a traditional method, which is superior to TSF (torque sharing function). In this paper, a new controller is proposed with modified hysteresis and PWM in DITC. With the modified algorithm, the torque error will be reduced with PWM in DITC. The proposed algorithm is effective to improve sample and 0 status in a period. The modified algorithm is based on application of zero in the asymmetric half bridge, which is implemented as the buffer zone. In case of big torque error, hysteresis will mitigate error under the characteristic of fast responding. In case of small errors, the modified PWM will solve torque error in the equivalent strategy, which has adopted the essence of the impulse equivalent method. The modified controller is designed to reduce responding time and minimize the torque ripple under the proposed algorithm, which has been designed to harness different functions in different torque errors. Based on final simulation and experimental results, responding speed and reduction of output torque ripple are enhanced effectively. [ABSTRACT FROM AUTHOR]

This paper analyzes the transverse vibration laws of 5000 m ladder-shaped mining pipe under different towing velocities and accelerations in the ocean, thinking of the pipe as the beam model, discretized based on the FEM. The algorithm is used to solve the problem to obtain the transverse vibration law. The research shows that the mining pipe overall transverse vibration trend decreases first and then increases, the minimum vibration value occurs at 3000 m, and the maximum occurs at the top. Increasing the towing velocity, acceleration, and ore bin weight will increase the transverse vibration value. The vibration intensity produced by the same acceleration in the constant acceleration and deceleration stages is different, and the damping effect after adding the same damping is also different. In the range of 0.01 m/s2–0.1 m/s2, the vibration reduction effect after adding damping in the constant deceleration stage is more significant, and in the range of 0.1 m/s2-0.2 m/s2, the vibration reduction effect after adding damping in the constant acceleration stage is more significant. In the stage of the constant acceleration or deceleration, when adding the same damping, the vibration intensity generated by the large acceleration is still far greater than the vibration intensity generated by the small acceleration, so the mining ship should keep the small acceleration for towing motion. [ABSTRACT FROM AUTHOR]

The basic notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation. This paper proposes an intuitionistic fuzzy rough set (IFRS) model which is a combination of intuitionistic fuzzy set (IFS) and rough set. We approximate an IFS by using soft binary relations instead of fixed binary relations. By using this technique, we get two pairs of intuitionistic fuzzy (IF) soft sets, called the upper approximation and lower approximation with respect to foresets and aftersets. Properties of newly defined rough set model (IFRS) are studied. Similarity relations between IFS with respect to this rough set model (IFRS) are also studied. Finally, an algorithm is constructed depending on these approximations of IFSs and score function for decision-making problems, although a method of decision-making algorithm has been introduced for fuzzy sets already. But, this new IFRS model is more accurate to solve the problem because IFS has degree of nonmembership and degree of hesitant. [ABSTRACT FROM AUTHOR]

Cubature Kalman filter phase unwrapping (CKFPU) is an effective algorithm in unwrapping the interferograms. The local phase slope estimation is a key factor that affects the unwrapped accuracy. However, the estimation accuracy of local phase slop is relatively low in high noisy and dense stripes areas, which usually leads to the unsatisfactory unwrapped results. In order to effectively solve this issue, the rewrapped map of the unwrapped phase (obtained by CKFPU algorithm), which is a filtered interferogram with clearer fringes and more detailed information, is proposed in this paper to improve the phase slope estimation. In order to solve the problem of imprecise error variance for the new phase slope estimation, an adaptive factor is introduced into the CKFPU algorithm to increase the stability and reliability of the phase unwrapping algorithm. The proposed method is compared with the standard CKFPU algorithm using both simulated and real data. The experimental results validate the feasibility and superiority of the proposed method for processing those high noise dense fringe interferograms. [ABSTRACT FROM AUTHOR]

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions. [ABSTRACT FROM AUTHOR]

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence. [ABSTRACT FROM AUTHOR]

Although the existing algorithms have done a lot of progress in extended target tracking (ETT), most algorithms are limited by the assumption of the target shape, and there are problems with parameter calculations. To solve the problems above, an extended target tracking algorithm based on level set and Gaussian Surface fitting (GSF), is proposed. Different from the existing extended target tracking algorithms, the proposed algorithm uses a level set to implicitly describe the evolution of the estimated shape, eliminates the parameterization process, and uses the GSF to construct the measurement source spatial distribution function (MSSDF) of the extended target. The proposed algorithm can obtain the estimated shape of the extended target without knowing the prior shape and size of the target. The simulation results verify the effectiveness and reliability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows' intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem. [ABSTRACT FROM AUTHOR]

Multiarea economic dispatch (MAED) is a vital problem in the present power system to allocate the power generation through dispatch strategies to minimize fuel cost. In economic dispatch, this power generation distribution always needs to satisfy the following constraints: generating limit, transmission line, and power balance. MAED is a complex and nonlinear problem and cannot be solved with classical techniques. Many metaheuristic methods have been used to solve economic dispatch problems. In this study, the dynamic particle swarm optimization (DPSO) and grey wolf optimizer (GWO) have been used to solve the MAED problem for single-area 3 generation units, a two-area system with four generating units, and four areas with 40-unit system. The hunting and social behaviors of grey wolves are implemented to obtain optimal results. During the optimization search, this algorithm does not require any information regarding the objective function's gradient. The tunable parameters of the original PSO that are three parameters are dynamically controlled in this work that provides the efficient cost values in less execution time although satisfying all the MAED problem's diverse constraints. In this study, the authors also implemented the GWO algorithm with two tunable parameters, and its execution is straightforward to implement for the MAED problem. [ABSTRACT FROM AUTHOR]

The flying networks provide an efficient solution for a wide range of military and commercial purposes. The demand for portable and flexible communication is directed towards a quick growth in interaction among unmanned aerial vehicles (UAVs). Due to the frequent change in topology and high mobility of vehicles, routing and coordination becomes a challenging task. To maximize the throughput of the network, this study addresses the UAV swarm's problems related to the coordination and routing and defines the proposed solution to solve these issues. For this, a network is assumed which contains an equal number of dynamic vehicles. It also presents the communication graph concept of UAVs and designs a fixed-wing UAV model to improve the efficiency of the network in terms of throughput. Furthermore, the proposed algorithm based on Cauchy particle swarm optimization (CPSO) aims towards the better performance of UAV swarms and aims to solve the combinatorial problem. The simulation results show and confirm the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]