With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level.

Improving the ability to detect small leaks to prevent more severe accidents plays an extremely important role in the safe operation of pipelines. To tackle the issue of low diagnostic accuracy associated with single sensors for detecting small leaks, a multi-source multi-modal feature fusion method for gas pipeline leak detection was proposed. First, the collected data from multiple sensors were transformed into two-dimensional time-frequency images for input into the feature extraction network. Then, the dual-information fusion (DIF) module was introduced, incorporating the attention mechanism and multi-scale feature fusion to enhance the model’s feature expression capability, and fully interact with the multi-modal features. Secondly, the channel split multiscale convolution (CSMC) module was designed to accommodate the diversity of input data and improve the model’s generalization capability. The DIF and CSMC modules were cascaded and fused to produce the classification results through the fully connected layer. Finally, the effectiveness of the proposed method was assessed using pipeline leak data collected in the laboratory. The experimental results demonstrate that the proposed multi-modal deep learning model can effectively identify the small leak state in pipelines, exhibiting superior diagnostic performance when compared to the current mainstream image classification models.

Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.

Natural gas pipelines are an important mode of transportation. How to quickly and accurately detect abnormal signals that occur during pipeline operation is crucial to judging the pipeline status. In this paper, an abnormal signal detection method based on improved YOLOv7 (YOLO-PD) was proposed to address the low detection accuracy of abnormal signals and the dependence on manual signal segmentation scale setting in pipeline operation. Firstly, a new refined channel attention (RCA) module was proposed to enhance network feature extraction. Secondly, a MaxPooling with RCA module was designed to strengthen network feature extraction and fusion capabilities. Furthermore, the atrous spatial pyramid pooling with RCA module was used to expand the model’s receptive field and enhance the core information features, thereby improving the detection accuracy of the model for multi-scale objects. Subsequently, the SIoU loss function was utilized to expedite network convergence and optimize the model training process. The experimental results demonstrate that YOLO-PD exhibits high accuracy and speed, with an mAP@0.5 of 0.996 and a frame rate of 65. In comparison to other prevalent target detection algorithms, the method proposed in this paper can effectively identify abnormal signals in pipelines and achieve higher detection accuracy.

Mammography is one of the most effective detection methods of breast cancer. A novel algorithm was proposed to segment the pectoral muscle automatically. It firstly carried out a series of ROIs upon the neighborhood, and in each ROI, the optimal threshold was computed with the iterative thresholding. After combining these thresholds into an optimal threshold curve and computing the corresponding local mean square deviation curve, the segmentation threshold of the pectoral muscle could be determined. Then, zonal Hough transform and polyline fitting were applied to fit the boundary of the pectoral muscle, and finally an elastic thread algorithm was used to extract the pectoral muscle accurately. This algorithm overcame the defects of the conventional ones, and could get a generally high precision.

This study presents a novel sensor that combines air pressure and temperature measurement using dual-interference microfiber resonators. The sensor leverages intermode interference and the resonant ring resonance effects within the microfiber loop structure, enabling simultaneous demodulation of temperature and air pressure. We have developed a mathematical model that is closely monitored alongside the reflectance spectral structure. The sensor exhibits two distinct linear sensitivities for pressure and two different linear sensitivities for temperature, as revealed by our testing results. Specifically, microfiber mode interference demonstrates a temperature and pressure sensitivity of 160 pm/°C and 167/kPa, respectively, while the ring resonance effect yields a temperature and pressure sensitivity of 132 pm/°C and 62 pm/kPa, respectively. The maximum temperature resolution stands at 0.025°C, and the pressure resolution is 0.023 kPa. These findings are supported by the concept of a linear connection, which serves as the foundation of the transmission matrix and enables concurrent discrimination of air pressure and temperature. This sensor offers advantages due to its straightforward structure, high extinction ratio, superior electrical safety, and effective protection against electromagnetic interference.