Path tracking control techniques are commonly used in automated traveling systems for mobile equipment, where the role is to control the mobile equipment to travel along a reference path. The selection of reference points on the reference path is critical for path tracking control. However, current methods for selecting reference points face difficulties in simultaneously ensuring the accuracy and real-time performance required for path tracking control. To solve the problem, a method for selecting path tracking control reference points based on rolling prediction is proposed. The principle of this method is to predict the search range of the next control period by parameters such as the reference point of the current control period, the speed of the mobile equipment, the control period, and the mileage interval between adjacent points of the reference path. Subsequently, a localized optimization technique identifies the point on the reference path closest to the mobile equipment and is selected as the reference point. Our proposed rolling prediction method demonstrates commendable performance in ensuring path tracking control’s accuracy and real-time capabilities. In the simulation results, the absolute value of the displacement error in path tracking control does not exceed 0.1872 m. Compared with the equal-interval point selection method, the magnitude of reduction in the absolute value of displacement error can achieve at least 33.12%. Additionally, the average value of the time consumed in each control period by the rolling prediction method does not exceed 0.0709 ms and does not more than 2.87% of the average value of the total time consumed by the path tracking controller in each control period. In contrast, the existing global optimization method can occupy up to 82.36% of the total time.

Every sensory neural system falls under the purview of large-scale neuroscience. The olfactory neural system, as a paradigm within this field, encounters challenges akin to other sensory models, including intricate model construction and the difficulty of aligning computational outcomes with experimental data. Some outcomes, despite their theoretical significance, demand excessive computational resources, presenting formidable barriers. Hence, unraveling the potential mechanisms of olfactory information processing and achieving precise odor identification remain daunting tasks. This article proposes a neural energy theory applicable to large-scale neuroscience research on odor recognition and coding in the olfactory system. Utilizing the W–Z neuron energy model, we developed a neural network model of the olfactory system based on its anatomical structure. By computing the total energy spike sequences for various odors in the piriform cortex and employing kernel function methods for odor pattern recognition in mixtures, we discussed the nonlinear energy coding characteristics of odors in the piriform cortex. Our findings suggest that utilizing the total energy of the olfactory system network for pattern recognition of external odor inputs can yield effective, straightforward, and reliable identification results. This research approach not only harmonizes computational outcomes of olfactory models across different levels but also offers the potential for analyzing and interpreting experimental data obtained at various levels within an energy-centric framework in the future. This underscores the advantage of large-scale neuroscience.

UAV detection and recognition in urban environment is an important part of civil UAV monitoring and countermeasures. This paper presents a method of target detection and extraction based on radar detection system. Firstly, a set of all-solid state portable surveillance radar system is designed based on modular design method. Then the strong clutter suppression method and the target detection method are researched. On this basis, the ICEEMDAN method and adaptive CFAR technology are adopted to reduce the false alarm rate and improve the detection accuracy. Finally, the results show that the proposed method can meet the requirements of high-precision detection of UAV in complex urban environment.

The traditional reclosing device cannot distinguish the fault nature of blind reclosing, which will cause secondary damage to the faulty power system. In addition, the existence of fault resistance makes fault location complicated. This paper proposes an automatic reclosing device which enters the fault line through the single-phase AC voltage signal of the power electronic converter. The device responds to the comprehensive utilization of the system harmonic impedance and locates the fault current through the comprehensive utilization of the system harmonic impedance judgment. So, the device can eliminate the influence of fault resistance for positioning the result. Because the measurement is performed only at the head-end circuit, no communication line is required. If a permanent fault is found in the system, the fault information and the latching circuit breaker should be indicated. If there is no fault notification, the circuit breaker should be switched. The equipment has been proved to have accurate identification to detect the results of various fault types, so it can avoid the secondary damage of the power system caused by blind reclosing.

三维激光扫描技术凭借测量误差小、测量耗时短的优势在各大领域里都得到广泛应用.使用此技术能够减少施工时间,提升工作效率.基于此优势,提出基于三维激光扫描的高层建筑物立面测量方法.采用三维激光扫描仪获取高层建筑物立面信息,并将获取的高层建筑物立面测量数据通过冗余数据处理、时间整合、空间整合、姿态纠正、分层处理以及特征提取六步骤完成高层建筑物立面测量.通过实验分析可知,该方法测量的数据与实际数据符合率均大于99.65％,与实际几乎完全相符,可应用于高层建筑立面测量工作中;与同类测量方法相比,该方法测量精度高达99.99％、测量耗时仅有10.33 ms,可高效率、高精度全面测量不同类型的高层建筑物立面信息.

Underground metal target detection refers to estimating the properties of underground metal targets based on a set of observed data. Electromagnetic induction (EMI) method and the least-squares inversion provide the data acquisition method and the parameter estimation method for the detection, respectively. As an important part of least-squares inversion, optimization algorithms directly affect the efficiency of the least-squares inversion. To improve the efficiency of underground metal target detection, it is necessary to compare the performance of different optimization algorithms. In this paper, we analyzed the characteristic of the complex EMI forward model using sensitivity analysis and inverse sensitivity analysis at first. Then, the EMI forward model and least squares were used to build the objective function. The estimation error, run time and number of iterations of six numerical optimization algorithms were compared under different signal-to-noise ratios (SNRs) and under different data acquisition spacing. The algorithms include gradient descent, steepest descent, Newton's method, Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, conjugate gradient method and Levenberg-Marquardt (LM) algorithm. A simulation platform was established to generate observed data and compare the optimization algorithms. The results of algorithm comparison showed that the BFGS, conjugate gradient and LM algorithm can efficiently and accurately estimate the properties of underground metal targets and the LM algorithm has the shortest run time and the least number of iterations. Finally, we carefully analyzed the time consumption of each optimization algorithm. The results show that the calculation of the gradient and step length selection greatly affect the efficiency of the optimization algorithms.

Underground target detection technology has been widely used in urban construction and resource exploration. With the development of industrial modernization, the demand for underground target detection is becoming more specific, such as the material and shape of underground targets. Therefore, it is necessary to classify the properties of underground targets. In this paper, sensitivity analysis was performed on the spheroid model and the approximate forward model at first, and the influence of the target properties on the model output is obtained. Secondly, we utilized the fitting algorithm to obtain the model parameters of the simulation data (model response of targets with varying shapes and materials), and analyzed the influence of the fitting algorithm on the classification results at different SNR. Finally, eight machine learning algorithms: support vector machine(SVM), neural network(NN), quadratic discriminant analysis (QDA), Gaussian process (GP), decision tree (DT), random forest (RF) and AdaBoost were used in this study to compare the obtained results. From the above analysis, we found that the shape (radius) have a greater influence on the model than the material (permeability) in the spheroid model. According to the approximate forward model, we found that it is not feasible to classify targets when the orientation is unknown. The influence of the fitting algorithm on the classification performances is related to the noise level. The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification. In the material-based classification, the classifier generally has a weaker ability to distinguish between permeable materials.

为了提高GM(1,1)模型在地铁施工过程中地表沉降量预测的精度,预防较大沉降或其他危险出现,提出了改进GM(1,1)模型预测方法.通过对比不同原始序列个数建立起来的预测值并确定最佳原始序列个数后,对GM(1,1)模型进行优化,并对构造背景值进行优化.通过缓冲算子对原始序列进行优化,之后再构造背景值进行优化.结果表明,背景值对模型的预测影响较小,缓冲算子在原始序列变化较大、变化不平顺时优化较好.

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

By studying tunnel face images collected during a highway tunnel excavation, we establish the classification system of photolithography by MATLAB programming. According to the rock mass index (rock block size index or RBI) concept, 19 virtual lines are arranged in turn to the tunnel contour line, and the difference of the brazier rock mass structure is evaluated synthetically by 19 RBs of the radial direction of the face. Based on the structure, the distribution of comprehensive index of the rock mass structure (Z-RBI) is obtained through a comprehensive evaluation of the rock surface, and the corresponding relationship between the Z-RBI and rock structure type is determined. Considering the hardening degree of rock mass, the Z-RBI, groundwater condition, and initial geostress state, we propose the BP classification method for tunnel rocks, and we evaluated the BT method by using the analytic hierarchy process standard in an actual project. The comparison of the BT classification method and traditional BQ method reveals that the result of the former is more in accordance with the actual level of the excavated rock face than that of the latter method.

为了对比分析不同地下水位下地铁车站在施工过程中的沉降变化情况,以长春地铁2号线解放大路车站为实际依托工程,建立了该地铁车站的有限元模型,并且将有限元计算结果与现场实测的施工沉降数据进行对比分析,验证了有限元模型的可靠性.分析了地铁车站中轴线上方的地表沉降在施工过程中的变化情况,并对上导洞、中导洞及1、2洞室拱顶沉降在施工过程中的变化情况进行了分析.通过定义3种不同地下水位工况,将不同地下水位下地铁车站的施工沉降曲线进行对比分析.结果 表明:地铁车站中轴线上方地表沉降的有限元计算结果和实测数据的最大误差仅为5.6％,地铁车站中轴线上方的地表沉降量呈现出阶梯式的变化趋势,随着地下水位高度的下降,拱顶沉降量的增长幅度存在突变情况.

利用基于信号强度值的方法确定不同环境下的无线信号路径损耗指数和其他参数,并根据路径损耗指数确定不同环境下的信号接收距离,实现无线传感网络的优化布局,以降低环境造成的信号衰落对无线通信系统性能的影响.在地下矿井环境中对提出的确定无线信号路径传输损耗参数的方法进行验证,实验结果表明,该方法得到的无线信号传输模型,能够为矿井中的WSN节点的合理布置提供依据.

For defective rusting on nuclear containment image,with lots of noise and interference similar in color,in this paper a rusting recognition algorithm of contour directed dilation is proposed.Using the characteristic yellowish orange color,extract basic rusting information through RGB multi-channel threshold segmentation.On the basis of rusting's annulus shape feature,the rusting by multi-direction according to contour's extension direction is divided.Contour fragment is dilated by the self-design eight-direction kernels in order to complement annulus rusting.The real rusting is recognized by roundness calculated according to contour moments.Experiments show that the algorithm can undo amounts of noise and interference similar in color so as to recognize and position the real rusting on the image.

Through the scientific research on WTN algorithm, the paper introduces an algorithm of WTN chess based on Monte-Carlo method and UCT algorithm, and puts forward an optimized method, which is used to generate and converge a game tree. The consequence of research shows that compared with WTN algorithm based on Minimax search algorithm and Alpha-Beta pruning algorithm, our optimized algorithm is much better in efficiency.

Underground metal target detection technology is widely used in various fields. One of the electromagnetic induction methods obtains the secondary field data in a region firstly and then uses the model inversion to estimate the parameters of metal targets. However, the traditional method must keep the detector horizontal in the detection process, and if not, the inversion results are inaccurate. Therefore, it is necessary to study the inversion algorithms for the dynamic detection environment. In this paper, a dynamic forward model was established for the dynamic detection environment at first. We utilized the attitude information of the detector to transform the observation coordinate system and then calculated the secondary field. Secondly, the objective function with these data was constructed for the optimization methods. Finally, two inversion algorithms, the conjugate gradient method and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, were used in this study to compare the obtained results. According to the simulation results and the performance of the algorithms so as to validate the feasibility of the proposed dynamic model and results meet the detection requirements.