培养学生“目标感”的策略

当前正处于信息快速更迭的时代,各种电子产品已经在小学生群体中“遍地开花”,成为“目标感”缺失越来越低龄化的“导火线”。语文学科核心素养是文化自信、语言运用、思维能力、审美创造的综合体现。本文将“目标感”与语文学科核心素养有机结合,探究在核心素养理念的指导下,教师如何在语文教学中培养学生的“目标感”,以强化小学生的语文综合素养。

Oriented Bounding Box Object Detection Model Based on Improved YOLOv8

In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.

Asynchronous Data Fusion of Two Different Sensors

An algorithm is presented for fusion of tracks created by radar and IR sensor which have different dimensional measurement data. It’s assumed that these sensors are asynchronous and the measurement data are transmitted to a central station at different rates. By means of the technique of time matching, two sets of asynchronous data are fused and then the filter is updated according to the fused information. The results show that the accuracy of the filter effect has been improved.

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

Classification of Underwater Target Echoes Based on Auditory Perception Characteristics

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.

Low sidelobe robust imaging in random frequency-hopping wideband radar based on compressed sensing

High resolution range imaging with correlation processing suffers from high sidelobe pedestal in random frequency-hopping wideband radar. After the factors which affect the sidelobe pedestal being analyzed, a compressed sensing based algorithm for high resolution range imaging and a new minimized ll-norm criterion for motion compensation are proposed. The random hopping of the transmitted carrier frequency is converted to restricted isometry property of the observing matrix. Then practical problems of imaging model solution and signal parameter design are resolved. Due to the particularity of the proposed algorithm, two new indicators of range profile, i.e., average signal to sidelobe ratio and local similarity, are defined. The chamber measured data are adopted to testify the validity of the proposed algorithm, and simulations are performed to analyze the precision of velocity measurement as well as the performance of motion compensation. The simulation results show that the proposed algorithm has such advantages as high precision velocity measurement, low sidelobe and short period imaging, which ensure robust imaging for moving targets when signal-to-noise ratio is above 10 dB.

A NOVEL REGION FEATURE USED IN MULTISENSOR IMAGE FUSION

A new region feature which emphasized the salience of target region and its neighbors is proposed. In region segmentation-based multisensor image fusion scheme, the presented feature can be extracted from each segmented region to determine the fusion weight. Experimental results demonstrate that the proposed feature has extensive application scope and it provides much more information for each region. It can not only be used in image fusion but also be used in other image processing applications.

Multi-objective optimization sensor node scheduling for target tracking in wireless sensor network

Target tracking in wireless sensor network usually schedules a subset of sensor nodes to constitute a tasking cluster to collaboratively track a target.For the goals of saving energy consumption,prolonging network lifetime and improving tracking accuracy,sensor node scheduling for target tracking is indeed a multi-objective optimization problem.In this paper,a multi-objective optimization sensor node scheduling algorithm is proposed.It employs the unscented Kalman filtering algorithm for target state estimation and establishes tracking accuracy index,predicts the energy consumption of candidate sensor nodes,analyzes the relationship between network lifetime and remaining energy balance so as to construct energy efficiency index.Simulation results show that,compared with the existing sensor node scheduling,our proposed algorithm can achieve superior tracking accuracy and energy efficiency.

EXPLORATION ON METHOD OF AUTO-CLASSIFICATION FOR MAIN GROUND OBJECTS OF THREE GORGES RESERVOIR AREA

Taking TM images, SPOT photos and DEM images as the basic information, this paper had not only put forward a kind of manual controlled computer-automatic extraction method, but also completed the task of extracting the main types of ground objects in the Three Gorges Reservoir area under relatively high accuracy, after finishing such preprocessing tasks as correcting the topographical spectrum and synthesizing the data. Taking the specialized image analysis software-eCognition as the platform, the research achieved the goal of classifying through choosing samples, picking out the best wave bands, and producing the identifying functions. At the same time the extraction process partly dispelled the influence of such phenomena as the same thing with different spectrums, different things with the same spectrum, border transitions, etc. The research did certain exploration in the aspect of technological route and method of using automatic extraction of the remote sensing image to obtain the information of land cover for the regions whose ground objects have complicated spectrums.

Prediction-based energy-efficient target tracking protocol in wireless sensor networks

A prediction based energy-efficient target tracking protocol in wireless sensor networks(PET) was proposed for tracking a mobile target in terms of sensing and communication energy consumption.In order to maximize the lifetime of a wireless sensor network(WSN),the volume of messages and the time for neighbor discovery operations were minimized.The target was followed in a special region known as a face obtained by planarization technique in face-aware routing.An election process was conducted to choose a minimal number of appropriate sensors that are the nearest to the target and a wakeup strategy was proposed to wakeup the appropriate sensors in advance to track the target.In addition,a tracking algorithm to track a target step by step was introduced.Performance analysis and simulation results show that the proposed protocol efficiently tracks a target in WSNs and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

Research on the Strategy of Underwater United Detection Fusion and Communication Using Multi-sensor

In order to solve the distributed detection fusion problem of underwater target detection, when the signal to noise ratio (SNR) of the acoustic channel is low, a new strategy for united detection fusion and communication using multiple sensors was proposed. The performance of detection fusion was studied and compared based on the Neyman-Pearson principle when the binary phase shift keying (BPSK) and on-off keying (OOK) modes were used by the local sensors. The comparative simulation and analysis between the optimal likelihood ratio test and the proposed strategy was completed, and both the theoretical analysis and simulation indicate that using the proposed new strategy could improve the detection performance effectively. In theory, the proposed strategy of united detection fusion and communication is of great significance to the establishment of an underwater target detection system.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

Generalized Approach to Signal Processing in CollaborativeWireless Sensor Systems for Target Detection

Collaboration in wireless sensor systems must be fault-tolerant due to the harsh environmental conditions at which such systems can be deployed. This paper focuses on finding the signal processing algorithms for collaborative target detection based on the generalized approach to signal processing （GASP） in the presence of noise. The signal processing algorithms are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor systems. Two types of generalized signal processing algorithms, namely, value fusion and decision fusion constructed according to GASP in the presence of noise, are identified first. When comparing their performance and communication overhead, the decision fusion algorithm is found to become superior to the value fusion algorithm as the ratio of faulty sensors to fault free sensors increases. The use of GASP under designing the value and decision fusion algorithms in wireless sensor systems allows us to obtain the same performance, but at low values of signal energy, as well as under employment of the universally adopted signal processing algorithms widely used in practice.

LOCALIZATION FOR MOBILE TARGET IN WIRELESS SENSOR NETWORKS

A precise localization for mobile target in wireless sensor networks is presented in this letter,where a geometrical relationship is explored to improve the location estimation for mobile target,in-stead of a simple centroid approach.The equations of location compensation algorithm for mobiletarget are derived based on linear trajectory prediction and sensor selective activation.The resultsbased on extensive simulation experiments show that the compensation algorithm gets better per-formance in metrics of quality of tracking and energy efficiency with the change of sensor sensing range,the ratio of sensing range and sensor activation range,and the data sampling rate than traditionalmethods,which means our proposing can achieve better quality-energy tradeoff for mobile target inwireless sensor networks.

MICRO-MOTION TARGET SENSING BY STEPPED-FREQUENCY CONTINUOUS-WAVE RADAR

Mechanical micro-vibration of a target may induce phase modulations on the radar echo,and the vibration can be detected to identify micro-motion target. In this paper,a new method to detect a micro-motion target and obtain its range and micro-motion frequency is proposed in which multiple periods stepped-frequency continuous-wave signal and Moving Target Indication (MTI) filter are adopted. The simulation results illustrate the validity of this method and present the detection of the target range and the vibration frequency. The experiment gives promising results.

Multiresolutional Maneuvering Target Tracking Fusion Algorithm Based on Singular Sensor and Multipale Models

Multiresolutional signal processing has been employed in image processing and computer vision to achieve improved performance that cannot be achieved using conventional signal processing techniques at only one resolution level [1,2,5,6] . In this paper,we have associated the thought of multiresolutional analysis with traditional Kalman filtering and proposed A new fusion algorithm based on singular Sensor and Multipale Models for maneuvering target tracking.

Questions to be reconsidered in teaching pronunciation

After systematically examining the recent development of research on teaching pronunciation in China, this paper has addressed several issues to be reconsidered in the context of pronunciation teaching and learning, such as the value, teaching objectives, ways of teaching, teaching emphasis and learners＇ affective needs. Based on these different aspects of teaching pronunciation, relevant suggestions are also provided.

Optimization and Control of Extractive Distillation with Heat Integration for Separating Benzene/Cyclohexane Mixtures

In this work, the extractive distillation with heat integration process is extended to separate the pressure-insensitive benzene-cyclohexane azeotrope by using furfural as the entrainer. The optimal design of extractive distillation process is established to achieve minimum energy requirement using the multi-objective genetic algorithm, and the results show that energy saving for this heat integration process is 15.7%. Finally, the control design is performed to investigate the system's dynamic performance, and three control structures are studied. The pressure-compensated temperature control scheme is proposed based on the first two control structures, and the dynamic responses reveal that the feed disturbances in both flow rate and benzene composition can be mitigated well.

Space luminous environment adaptability of missile-borne star sensor

To solve the problem of stray interference to star point target identification while a star sensor imaging to the sky, a study on space luminous environment adaptability of missile-borne star sensor was carried out. By Plank blackbody radiation law and some astronomic knowledge, irradiancies of the stray at the star sensor working height were estimated. By relative astrophysical and mathematics knowledge, included angles between the star sensor optical axis point and the stray at any moment were calculated. The calculation correctness was verified with the star map software of Stellarium. By combining the upper analysis with the baffle suppression effect, a real-time model for space luminous environment of missile-borne star sensor was proposed. By signal-noise rate （SNR） criterion, the adaptability of missile-borne star sensor to space luminous environment was studied. As an example, a certain type of star sensor was considered when imaging to the starry sky on June 22, 2011 （the Summer Solstice） and September 20, 2011 （August 23 of the lunar year, last quarter moon） in Beijing. The space luminous environment and the adaptability to it were simulated and analyzed at the star sensor working height. In each period of time, the stray suppression of the baffle is analyzed by comparing the calculated included angle between the star sensor optical axis point and the stray with the shielded provided by system index. When the included angle is larger than the shielded angle and less than 90~, the stray is restrained by the baffle. The stray effect on star point target identification is analyzed by comparing the irradiancy of 6 magnitude star with that of the stray on star sensor sensitization surface. When the irradiancy of 6 magnitude star is 5 times more than that of the stray, there is no effect on the star point target identification. The simulation results are identicat with the actual situation. The space luminous environment of the missile-borne star sensor can be estimated real-timely by this model. The adaptability of the star sensor to space luminous environment can be analyzed conveniently. A basis for determining the relative star sensor indexes, the navigation star chosen strategy and the missile launch window can be provided.

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.