In this paper, we propose a mathematical model to solve the multi-objective cargo allocation problem with greater stability and efficiency; the model for cargo allocation maximizes the total cargo weight, minimizes the total number of trips, minimizes the number of cargo loading and unloading points, and offers fast convergence based on the elitism genetic algorithm (FEGA). First, a hierarchical structure with the Pareto dominance relation and an elitism retention strategy were added on the basis of the genetic algorithm. This helped to improve the population diversity while accelerating the local search ability of the algorithm. Then, the random structure of the initial population was modified, and a double population strategy was designed. An adaptive operation was subsequently added to sequentially improve the global search ability of the algorithm and accelerate the convergence speed of the population. Based on the new algorithm, real cargo data were used to demonstrate the feasibility and optimization potential of the new method. The results show that compared with the traditional genetic algorithm, the proposed algorithm has a better optimization effect in solving the cargo allocation process with strong constraints and a large search space; the search performance and convergence, moreover, are also improved.
With the rapid proliferation of technology, the degree of informatization in the financial industry continues to increase. The integration of financial data with power marketing platforms, moreover, is accelerating the interaction between users and power marketing platform data (e.g., basic customer details, energy metering data, electricity fee recovery data). The increased interaction, however, leads to higher data transmission leakage which can result in incorrect formulation of power usage strategies and electricity prices. Therefore, to satisfy the security requirements for data interaction in power marketing systems and ensure economic benefits for the power company, we propose an Ordered Binary Decision Diagram (OBDD) based on Ciphertext Policy Attribute Based Encryption (CP-ABE). This multi-level access approach can reduce the autonomy of shared data authority control in the remote terminal unit and improve the efficiency of data access. In addition, based on security and performance analysis, the proposed access control scheme is both more efficient and more secure than other schemes.
Knowledge graphs are an effective way to structurally represent and organize unstructured knowledgeare; in fact, these graphs are commonly used to support many intelligent applications. However, product-related knowledge is typically massive in scale, heterogeneous, and hierarchical; these characteristics present a challenge for traditional knowledge query processing methods based on relational and graph models. In this paper, we propose a solution to address these challenges by designing and implementing a product knowledge query processing method using CPU and GPU collaborative computing. Firstly, in order to leverage the full parallel computing capability of GPU, a product knowledge storage strategy based on a sparse matrix is proposed and optimized for the scale of the task. Secondly, based on the storage structure of the sparse matrix, a query conversion method is designed, which transforms the SPARQL query into a corresponding matrix calculation, and extends the join query algorithm to the GPU for acceleration. In order to verify the effectiveness of the proposed method, we conducted a series of experiments on an LUBM dataset and a semisynthetic dataset of products. The experimental results showed that the proposed method not only improves retrieval efficiency for large-scale product knowledge datasets compared with existing RDF query engines, but also achieves better retrieval performance on a general RDF standard dataset.
Accurate classification of power system customers can enable differentiated management and personalized services for customers. In order to address the challenges associated with accurate customer classification, this paper proposes a classification method based on an equilibrium optimizer and an extreme learning machine. In this method, an adaptive competition mechanism is proposed to balance the global exploration and local mining ability of an equilibrium optimizer, improving the performance of algorithms in finding optimal solutions. Thereafter, the proposed equilibrium optimizer is integrated with an extreme learning machine to classify the customers of a power system. Experiments on real data sets showed that the proposed algorithm integrated with an extreme learning machine offers more accurate performance for different classification indexes; hence, the proposed method can provide an effective technical means for power system customer management and service.
Traditional worker helmet wearing detection models commonly used at construction sites suffer from long processing times and high hardware requirements; the limited number of available training data sets for complex and changing environments, moreover, contributes to poor model robustness. In this paper, we propose a lightweight helmet wearing detection model—named YOLO-S—to address these challenges. First, for the case of unbalanced data set categories, a hybrid scene data augmentation method is used to balance the categories and improve the robustness of the model for complex construction environments; the original YOLOv5s backbone network is changed to MobileNetV2, which reduces the network computational complexity. Second, the model is compressed, and a scaling factor is introduced in the BN layer for sparse training. The importance of each channel is judged, redundant channels are pruned, and the volume of model inference calculations is further reduced; these changes help increase the overall model detection speed. Finally, YOLO-S is achieved by fine-tuning the auxiliary model for knowledge distillation. The experimental results show that the recall rate of YOLO-S is increased by 1.9% compared with YOLOv5s, the mAP of YOLO-S is increased by 1.4% compared with YOLOv5s, the model parameter is compressed to 1/3 of YOLOv5s, the model volume is compressed to 1/4 of YOLOv5s, FLOPs are compressed to 1/3 of YOLOv5s, the reasoning speed is faster than other models, and the portability is higher.
With the increasing popularity of sensors, time-series data have attracted significant attention. Early time series classification (ETSC) aims to classify time-series data with the highest level of accuracy and smallest possible size. ETSC, in particular, plays a critical role in fintech. First, this paper summarizes the common classifiers for time-series data and reviews the current research progress on minimum prediction length-based, shapelet-based, and model-based ETSC frameworks. There are pivotal technologies, advantages, and disadvantages of the representative ETSC methods in separate frameworks. Next, we review public time-series datasets in fintech and commonly used performance evaluation criteria. Lastly, we explore future research directions pertinent to ETSC.
Electricity theft results in significant losses in both electric energy and economic benefits for electric power enterprises. This paper proposes a method to detect electricity theft based on t-LeNet and time series classification. First, a user’s power consumption time series data is obtained, and down-sampling is used to generate a training set. A t-LeNet neural network can then be used to train and predict classification results for determining whether the user exhibits behavior reflective of electricity theft. Lastly, real user power consumption data from the state grid can be used to conduct experiments. The results show that compared with the time series classification method based on Time-CNN (Time Convolutional Neural Network) and MLP (Muti-Layer Perception), the proposed method offers improvements in the comprehensive evaluation index, accuracy rate, and recall rate index. Hence, the proposed method can successfully detect electricity theft.