bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Energy Management of Pressurized Oxygen-Supplemented Buildings
Authors: Zhang Kun , Wang Kaiqiang , Zhou Hao , Lu Jianfeng , Liu Weijun , Zhou Jiyun , Karimzadeh Sina 
Published: August 2023

Abstract
The energy system of supercharged and oxygenated buildings is a hybrid energy system. The energy used is solar energy, electric energy, pressure energy, etc. In order to make the hybrid energy system more stable and controllable, and to achieve the purpose of energy saving under the condition of stable power consumption of supercharged and oxygenated buildings, a power consumption prediction and management model of supercharged and oxygenated buildings based on neural network is proposed. According to the power consumption system structure of the pressurized oxygenation building, this model establishes a power consumption prediction model based on neural network, and then verifies by simulation that the model can better meet the power consumption prediction conditions of the pressurized oxygenation building. Finally, according to the experiment, this power consumption prediction model can make the power consumption management of the pressurized oxygenation building more stable and controllable. When meeting the daily and stable power consumption requirements of the pressurized oxygenation building, the model can be used to predict the power consumption of the pressurized oxygenation building. Green energy is also reasonably used to save energy in pressurized and oxygen-supplemented buildings.
An Novel Technique for Wireless Power Transfer Systems Based on the Parametric Set
Authors: Du Juan , Lan Jianyu , Wei Qingxin , Xie Meng , Zhao Junli 
Published: August 2023

Abstract
High efficiency and high power level are important indicators of the Inductive Power Transfer (IPT) system. However, the problem of optimizing the efficiency of IPT has not been well solved, and improving control methods is one of the effective ways to solve this problem. In order to improve the existing secondary-side control scheme and achieve the goal of simultaneously achieving optimal transmission efficiency and stable output, this paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. This paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. The paper develops a model of the IPT system to find the best load, then analyzes the active rectifier, phase shift angle, and load relationship to create a parameter set for controlling quantities like α and β. From there, a new control strategy is proposed and its effectiveness is confirmed through simulations. The proposed strategy enhances transmission efficiency and output stability of the IPT system, making it highly applicable in areas like electric vehicle charging and wireless charging devices.
Design of a High Performance Vector Processor Based on RISIC-V Architecture
Authors: Han Xiaojing , Liu Liang , Zhang Zhe , Sun Yufeng , Zhou Jiahui , Cai Hao 
Published: August 2023

Abstract
This paper proposes a high performance Vector processor based on the high performance Embedded Core which is named TS800. The TS800 is a 4-core processor based on RISC-V architecture, implements IMAFDV instruction set, supports L2 Cache, branch prediction, sequential pipeline, and dual-issue structure. The traditional CPU mainly supports Scalar calculations, or only supports Vector calculations. For applications such as image and signal processing, there are a large number of data parallel computing operations. To solve the problem of low performance of parallel data calculations in industrial power applications, it is proposed to add VPU hardware implementation in the TS800. The TS800 can support FFT algorithm, adaptive controllers Reinforcement learning and learning-based underlying algorithm requirements. In this paper, the module and data flow between each processing unit and the control circuit, that is, the hardware realization of VPU module are proposed. Large-area units such as float arithmetic, multiplication and division are multiplexed with the Scalar operator in the CPU, while the control circuit is placed in the VPU-ALU, and the area is small. Units such as arithmetic and logic operation instructions, shift operation instructions, comparison operation instructions, and permutation instruction are implemented through the VPU-ALU, which makes the overall design area smaller and the performance better. At the same time, through the fir, fft, conv, matrix, Signal Converge and variance test, it is proved that while executing the same program, the running time of the cpu only with Scalar is 1.44 to 9.55 times that of the CPU with Vector module, which can support the underlying algorithm of the adaptive controller.
Automatic Relay Protection Calibration Device and System Design for New Intelligent Power Grid
Authors: Dai Yang , Li Dongmei , Chen Yuxin , Tang Mengqin 
Published: August 2023

Abstract
Maintaining the protection device and eliminating the abnormal and fault defects of the device are important tasks for the maintenance of the power system. In general, relay protection equipment managers need to regularly check the relay protection device. According to the usual practice, relay protection professionals use the relay protection tester to manually connect. The traditional relay protection verification method has complicated operation, high professional requirements for testers. In this paper, a set of intelligent relay protection verification device with high degree of automation and harmonious human-computer interaction is developed to realize the communication between the host computer and the protection device and the tester. The host computer software and database were developed. The thermal overload protection in the SPAM150C motor protection device of ABB company is verified, and the reliability of the device is verified. The device can improve the efficiency of relay protection equipment inspection, reduce the technical threshold of operators, and reduce the probability of human error. It realizes the process and standardized control of the operation process of relay protection verification which has strong practical application value.
Design and Development of Ground Wire Repair Robot for Transmission Line
Authors: Zhao Hanghang , Fan Min , Pei Shaotong , Zheng Wulue , Fang Jing , Liang Weixin 
Published: August 2023

Abstract
To ensure the normal and stable operation of the power system, it is important to repair broken and loose strand lines in a timely manner. In view of the limitations and shortcomings in repairing broken and loose strands of overhead transmission lines, this paper designs a set of robots that can automatically repair overhead transmission lines online. First, research and design for the mechanical system of the ground wire repair device for overhead transmission lines, including the walking system and the pre-twisted wire winding system; then develop corresponding software and hardware control system, including the data, image transmitting, receiving modules and the operating system of the device, and develop corresponding control devices, mobile phone software and other wireless terminal control systems. The ground wire repair device of the overhead transmission line developed by the project meets the basic requirements for the repair of broken and loose strands of the line, solved a series of problems caused by current manual repair methods, improves the automation of the operation, and has the significance of promotion and application.
Assessment of Oil Chromatography Sensor Failure for Transformer Based on Multi-dimensional Data-driven
Authors: Sha Weiyan , He Ninghui , Zhang Fangbo 
Published: August 2023

Abstract
Online monitoring and analysis of transformer oil chromatography is of great significance for the assessment of internal insulation with transformer operation. The existence of numerous invalid monitoring sensors in the field makes the quality of online monitoring data degraded, which makes the online monitoring system unable to monitor the operating status of transformers in a timely and accurate manner. This paper propose a data-driven method for evaluating the effectiveness of oil chromatography monitoring sensors by selecting a fixed-length feature data set from online data for evaluation, and then using the distribution of abnormal values, the distribution of consecutive identical values, the variation of coefficient of variation and the variation of gas production rate in the feature data set as criteria to judge the feature data set and obtain the corresponding discriminant values. Afterwards, the discriminant values are assigned weights according to the focus of each criterion to obtain the sensor status values, which are compared with the preset tolerance level to obtain the sensor evaluation results. The method proposed evaluates the effectiveness of online monitoring sensors from several aspects, which can detect faulty sensors in time and is of great significance for improving the accuracy of online monitoring data and the reliability of monitoring device.
Research on Indoor Wireless Positioning Method based on Center-of-mass Algorithm
Authors: Wang Yutang 
Published: August 2023

Abstract
Indoor mobile positioning is widely used in shopping malls, energy saving in buildings, medical care and emergency rescue. Range-free algorithms typically use the connectivity of a wireless sensor network system to estimate the location of target nodes, typically through Dv-hop, the prime method, ap-proximate point-in-triangulation test, or APIT for short. -free algorithms can be obtained by changing the distribution density of reference nodes or by changing the blind node communication radius to obtain higher accuracy. Due to constraints such as complex indoor environments, current indoor positioning methods are often less accurate and more expensive. To address these issues, this paper improves on the traditional center-of-mass algorithm by using a six-point center-of-mass algorithm for indoor positioning to further improve the accuracy of the indoor positioning algorithm. To this end, the following work is accomplished in this paper: 1. introduced the algorithms currently used for indoor positioning and their shortcomings, laying the theoretical foundation for the improved approach proposed in the following paper. 2. introduced the basic working principle of the center-of-mass algorithm and proposed an improved system using the six-point center-of-mass algorithm. 3. established the best possible model through experiments, then input test data and evaluated it based on expert judgement. The experimental results show that the model proposed in this study has excellent accuracy in indoor wireless positioning.
A Strategy for Predicting Residual Service Life of Batteries Based on Impedance Spectrum
Authors: Sun Dexing , Jian Zhichao , Li Xiaosha , Li Shaoliang , Ouyang Ling , Liu Jianda , Lyu Chao , Chen Yeh-Cheng 
Published: August 2023

Abstract
With the wide application of lithium batteries in electric vehicles, energy storage and other fields, accurate life prediction of lithium ion batteries becomes particularly important. As a fast and non-destructive electrochemical technology, impedance spectroscopy can reflect the internal characteristics of Li-ion batteries, and it is widely used in battery management. A method based on the health characteristic parameters extracted from the impedance spectrum of Li-ion battery is proposed in this paper to predict the battery life. Firstly, the equivalent circuit model is used to fit the impedance spectrum of the battery and screen out the characteristic quantity which can reflect the aging state of the battery. Then, BP neural network was used to establish the relationship model between the two. Finally, particle filter algorithm was used to predict the remaining service life of the battery. At the end of this paper, the measured results are compared with the predicted results to verify the effectiveness of the proposed method.
Research on Thermal Infrared Imaging and Positioning Technology for Emergency Rescue at Sea in Bad Weather
Authors: Wang Yilin , Zhang Shouxiang , Lu Chuanfa , Sha Jinzhao , Zhang Hangran 
Published: August 2023

Abstract
In bad weather, the visibility at sea is low, and it is difficult to rescue people and ships in distress. Existing sensors such as visible light cameras and LIDAR will fail in bad weather, so this paper proposes a design scheme for maritime emergency rescue based on thermal infrared cameras. Infrared thermal imaging is a technology that converts infrared thermal radiation into electrical signals and displays them through the screen, which is unaffected in low visibility environments such as bad weather and night and still works normally, and is a great aid to maritime emergency rescue. The accurate identification of maritime vessels is achieved by selecting the YOLOV5 target detection algorithm with high accuracy, the accurate ranging of maritime vessels by monocular ranging algorithm, and finally the whole project is deployed to NVIDIA Jetson AGX Xavier high-performance edge computing device for inference acceleration using TensorRT framework. To verify the effect of different sizes of network structures on the training of the dataset, experiments were conducted on YOLOV5’s s-model, m-model, l-model and x-model, respectively, and the results showed that the detection results of these four models were comparable, however, the inference frame rates did vary greatly. In windows the inference frame rate is 19fps for the s-model, 15fps for the m-model, 9fps for the l-model and 4fps for the x-model. The s-model was chosen to be used in the subsequent study. The s-model was accelerated by TensorRT to increase the frame rate from 19fps to 30fps, enabling real-time detection and ranging functions. The experimental analysis shows that the scheme of this paper is feasible and reliable.
Vehicle Detection Using the Light Weight Model Based on YOLOv5
Authors: Lyu Aihong 
Published: August 2023

Abstract
Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.
Deep Learning based Pavement Crack Detection System
Authors: Yu Lingjun , Li Qi 
Published: August 2023

Abstract
The pavement crack causes the highway service life to shorten, the safety hidden danger to increase. The low efficiency and high cost of manual inspection makes it difficult to detect pavement cracks. This paper proposes a fast and efficient deep learning pavement crack detection system. CRACK2000, an image segmentation dataset with complex interference background and multiple crack types, is constructed based on perspective transformation and image cropping. The scheme corrects the pavement crack images by perspective transformation. The extraction of pavement crack depth features is completed by applying the U-Net network. Finally, the pavement condition index PCI (pavement condition index) is calculated by quantifying the different types of crack information based on the segmentation results. The experimental results show that the Precision, Recall, F1-score and AUC of the U-Net network are 76.67%, 72.32%, 74.43% and 99.46% respectively. The AUC values reflect that the method is more capable of filtering out complex background interference from cracked images. The automatic pavement crack detection system designed in this paper can accurately locate and classify the location and category of pavement cracks, and perform quantitative pavement evaluation to obtain the pavement deterioration of the road section and the corresponding repair recommendations, enhancing the practicality of pavement crack detection.
TOFD Defect Image Recognition Based on YOLOv7
Authors: Fan Xiongfang , Tan Jiawan , Zhang Jianfeng , Gong Yanfeng , Zhang Hairui , Huang Chunsen , Chen Junlong 
Published: August 2023

Abstract
Welding technology is an important link in shipbuilding, and ultrasonic diffraction time difference method is one of its important inspection means, but the identification of its TOFD image still mainly relies on manual evaluation. In this paper, in order to solve the problems of low accuracy and unbalanced categories of defect recognition in the existing automatic identification research, by reclassifying defects into primary defects, secondary defects, and tertiary defects according to the degree of harm to the weld, and constituting a TOFD image dataset containing 934 images, and then using the YOLOv7 model, by adding mixup enhancement combined with mosaic enhancement, the The mAP value of the improved YOLOv7 model was improved by about 42% to 93.41% compared with the original model, and the AP0.5 values of the YOLOv7 improved model for primary defects, secondary defects, and tertiary defects were improved by about 30%, 38%, and 58% compared with the original model, respectively, by introducing the CBAM attention mechanism method to optimize the problems of insufficient data sample size and the imbalance between foreground and background; the AP 0.75 values improved by about 16%, 30%, and 45%, respectively, compared with the original model; its detection speed was 7.8 sheets per second.
A Comparative Study of Contrastive Learning-Based Few-Shot Unsupervised Algorithms for Efficient Deep Learning
Authors: Zhu Yancheng , Wu Qiwei , Liu Jianzi 
Published: August 2023

Abstract
The rapid development in computer vision algorithms, particularly in target detection algorithms based on deep neural networks, has led to significant advancements in various fields. However, recent research on target detection algorithms has shifted towards small sample scenarios with long-tailed distribution of categories, where achieving high accuracy target detection with limited data has become a crucial research topic. Deep neural networks often face several challenges when dealing with limited data, including convergence problems, overfitting, and poor generalization performance. In such scenarios, categories with little data are easily overwhelmed by the negative gradients generated by other categories during network training, which affects the final detection results. To address this issue, an unsupervised contrast learning algorithm has been proposed that can achieve accurate results on a small number of reference datasets without requiring a large number of datasets. One recently proposed contrast learning algorithm is PixelCL, which combines information from image depth in pretraining to improve results. The results of this algorithm show that it can obtain more accurate results through contrast learning with limited sample data. These findings highlight the potential of contrast learning algorithms in addressing negative gradient issues and improving the performance of deep neural networks in target detection tasks with limited data.
Improving Tibetan End-To-End Speech Recognition with Transfer Learning
Authors: Li Yunpeng , Zhang Qinya , Li Guanyu 
Published: August 2023

Abstract
End-to-end architecture has shown outstanding performance in the field of speech recognition, but achieving such performance typically requires a large amount of annotated data. In languages with sufficient corpus and resources, satisfactory recognition results have been achieved. However, for some low-resource data with insufficient training data, the lack of training data remains a bottleneck in building speech recognition systems. This paper presents an approach that utilizes self-supervised feature extraction and transfer learning to enhance the performance of acoustic models in low-resource languages. The proposed strategy involves retraining the fundamental acoustic model, originally trained on resource-rich languages, using a limited amount of low-resource speech data within an end-to-end architecture. By doing so, it aims to construct an improved acoustic model tailored for low-resource languages. The results of the model in the Tibetan language dataset show significant improvement, with a relative decrease of 26% in word error rate from 13.8% to 10.2% on the test set.
On UV Photon Distribution of Discharge in GIS Busbar Based on Ray Tracing
Authors: Shao Mingxin , Du Fei , Yuan Shuai , Bi Jiangang , Xu Yuan 
Published: August 2023

Abstract
In order to clarify the distribution law of the ultraviolet photon field in gas insulated switchgear busbar, meanwhile to correct the detection range of the ultraviolet sensor, the optical model of the gas insulated switchgear busbar based on the uniform theory of diffraction is proposed based on ray tracing. the distributions of the radiation ultraviolet fields are calculated when partial discharge occurs at the key positions such as basin insulator and conductor. Based on the average irradiance, maximum irradiance, and attenuation characteristics, the distribution law in the busbar is further analyzed, which provides a new method to confirm the location of ultraviolet sensors.
Simulation study of DBD discharge characteristics and active particle-assisted methane combustion
Authors: Zhao Zhibo , Song Peng , Su Jing 
Published: August 2023

Abstract
This paper established a model of the coaxial dielectric barrier discharge (DBD). Ar-CH4 is fed into the mixture as a working gas. Using 10.0 kHz as power research in different voltage and dielectric materials and medium thickness has influences on producing active particle mole fraction. The simulation results show that when the voltage increase, the molar concentration of O3 and H energetic particles increase obviously. When the thickness of the medium increases, the molar concentration of O3 and H decreases. When the dielectric constant of the dielectric material increases, the molar concentration of O3 and H also increases. The molar concentration of OH active particles also changed, but the law was unclear. At the same time, when the discharge voltage is 15 kV, the dielectric thickness is 2 mm, the dielectric constant is 3.75, and the DBD discharge is uniform and stable, the molar concentration of O3 and H is 0.069% and 0.056% respectively. The corresponding concentrations of O3 and H active particles were introduced into the established methane combustion reaction model. The study found that the joining of O3 and H energetic particles make the reaction begin to move significantly ahead of time. That has a promoting effect on the rate of the methane combustion reaction. In the methane combustion reaction model with different equivalence ratios, O3 with a molar concentration of 0.069% and H with a molar concentration of 0.056% introduced, it is concluded that these two active particles also have a tremendous promoting effect on the expansion of the thin burning limit of methane.