Information Technology and Control最新文献

Adaptive clustering object detection method for UAV images under long-tailed distributions 长尾分布条件下无人机图像的自适应聚类目标检测方法

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.33460

Guoxiang Li, Xuejun Wang, Yun Li, Zhitian Li

{"title":"Adaptive clustering object detection method for UAV images under long-tailed distributions","authors":"Guoxiang Li, Xuejun Wang, Yun Li, Zhitian Li","doi":"10.5755/j01.itc.52.4.33460","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.33460","url":null,"abstract":"UAV images are characterized by small targets, difficult to identify in the background image, clustering and sparse distribution of targets, etc. Many researchers have proposed the clustering target detection method (ClusDet) for UAV images. However, due to the large differences in target scales and uneven distribution of targets in UAV images, showing long-tailed distribution, the traditional ClusDet algorithm tends to truncate large and medium targets in the process of clustering; in the detection process, the fixed-threshold NMS method in the ClusDet algorithm is difficult to adaptively detect targets of different sizes, clustering and mutual occlusion. To address the above problems, this paper proposes an adaptive clustered target detection algorithm based on UAV images under long-tail distribution. The method is divided into three sub-networks: the adaptive clustering sub-network, which outputs several segmented images of small target clustering regions by extracting potential small target clustering regions in UAV aerial images; the segmentation and filling sub-network, which fills the images with disproportionate aspect ratio for the output of the adaptive clustering network to keep the size of the images within the reasonable range required by the detection network; and the detection sub-network, which detects the targets within the reasonable range required by the detection network by introducing attention mechanism, using variable threshold NMS, and training using sample balancing strategy effectively improve the detection accuracy of targets in the clustered region. Trained in VisDrone 2019 dataset, the simulation results show that the UAV image adaptive clustering target detection method based on long-tailed distribution has a large improvement in the detection accuracy of small targets, and can effectively improve the detection accuracy of the model for targets in the aggregation region, while the model has good generalization ability.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"7 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model construction of big data asset management system for digital power grid regulation 数字电网调控大数据资产管理系统模型构建

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.32642

Min Xu, Guanyu Zhang, Lin Duan

{"title":"Model construction of big data asset management system for digital power grid regulation","authors":"Min Xu, Guanyu Zhang, Lin Duan","doi":"10.5755/j01.itc.52.4.32642","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.32642","url":null,"abstract":"Abstract: There are many and complex big data for digital power grid regulation, which makes it more difficult to manage big data assets. Therefore, a model of big data asset management system for digital power grid regulation has been built. The model consists of three parts: data collection, data security storage and data index. The data acquisition architecture is designed, and the grey prediction method is used to fill the missing values and correct the abnormal values of the data acquisition results. Store the filled and amended data in the blockchain to ensure data security. The AR-tree index organization is used to realize the digital grid regulation big data index and achieve the goal of high-quality management of digital grid regulation big data assets. The experimental results show that the average recall and precision of this method are 96.9% and 97.9% respectively, and the data collection quality is high; After the application of this method, there is almost no non security data, and the proportion of security data is higher, which shows that this method can ensure the security of big data storage; The response time of digital power grid regulation big data index is less than 0.21s, and the index efficiency is higher.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"6 11","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Apply Physical System Model and Computer Algorithm to Identify Osmanthus Fragrans Seed Vigor Based on Hyperspectral Imaging and Convolutional Neural Network 基于高光谱成像和卷积神经网络，应用物理系统模型和计算机算法识别桂花种子活力

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.34476

Caihua Qiu, Feng Ding, Xiu He, Mengbo Wang

{"title":"Apply Physical System Model and Computer Algorithm to Identify Osmanthus Fragrans Seed Vigor Based on Hyperspectral Imaging and Convolutional Neural Network","authors":"Caihua Qiu, Feng Ding, Xiu He, Mengbo Wang","doi":"10.5755/j01.itc.52.4.34476","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.34476","url":null,"abstract":"This study explored the feasibility of using hyperspectral imaging technology and to identify Osmanthus fragrans seeds with different vigor under computer algorithm and physical system. Two varieties of Osmanthus seeds (JinQiGui and RiXiangGui) were artificially aged and then hyperspectral data were collected. Multivariate scattering correction (MSC) was used for spectral preprocessing. The selection of characteristic wavelength was realized by competitive adaptive reweighted sampling algorithm (CARS). The extreme learning machine (ELM) and k-nearest neighbor (KNN) were used to establish the spectral discriminant model, and convolutional neural network was used in the computer image discriminant model. The results show that the ability to recognize different vigor JQG was better than RXG. MSC preprocessing can not only make the data distribution more aggregated, but also effectively improve the accuracy of the model. MSC+CARS combined with discriminant model can be realized close to 100% recognition with fewer bands. Compared with machine learning model, image- depth learning model can get higher model accuracy for different vigor JQG and RXG without complex preprocessing. These results indicate that hyperspectral imaging technology can effectively distinguish different vigor of Osmanthus fragrans seeds based on computer technology and physical system, which is of great significance for future research.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"4 21","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations CAugment：通过组合图像处理操作实现数据集多样化的方法

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.33828

Wuliang Gao

{"title":"CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations","authors":"Wuliang Gao","doi":"10.5755/j01.itc.52.4.33828","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.33828","url":null,"abstract":"In deep learning, model quality is extremely important. Consequently, the quality and the sufficiency of the datasets for training models have attracted considerable attention from both industry and academia. Automatic data augmentation, which provides a means of using image processing operators to generate data from existing datasets, is quite effective in searching for mutants of the images and expanding the training datasets. However, existing automatic data augmentation techniques often fail to fully exploit the potential of the data, failing to balance the search efficiency and the model accuracy. This paper presents CAugment, a novel approach to diversifying image datasets by combining image processing operators. Given a training image dataset, CAugment is composed of: 1) the three-level evolutionary algorithm (TLEA) that employs three levels of atomic operations for augmenting the dataset and an adaptive strategy for decreasing granularity and 2) a design that uses the three-dimensional evaluation method (TDEM) and a dHash algorithm to measure the diversity of the dataset. The search space can be expanded, which further improves model accuracy during training. We use CAugment to augment the CIFAR-10/100 and SVHN datasets and use the augmented datasets to train the WideResNet and Shake-Shake models. Our results show that the amount of data increases linearly along with the training epochs; in addition, the models trained by the CAugment-augmented datasets outperform those trained by the datasets augmented by the other techniques by up to 17.9% in accuracy on the SVHN dataset.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"58 4","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weight Coefficient Based Adaptive Federated Learning for Vehicular Data Transmission 基于权重系数的车载数据传输自适应联合学习

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.34479

Hui Xie

引用次数: 0

Multidisciplinary Performance Enhancement on a Fixed-wing Unmanned Aerial Vehicle via Simultaneous Morphing Wing and Control System Design 通过同步变形机翼和控制系统设计提高固定翼无人飞行器的多学科性能

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.33527

Yüksel Eraslan, Tugrul Oktay

{"title":"Multidisciplinary Performance Enhancement on a Fixed-wing Unmanned Aerial Vehicle via Simultaneous Morphing Wing and Control System Design","authors":"Yüksel Eraslan, Tugrul Oktay","doi":"10.5755/j01.itc.52.4.33527","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.33527","url":null,"abstract":"An aerial vehicle design process usually aims to maximize performance in a specific flight phase regarding a particular topic such as aerodynamics, flight qualities, or control. This paper proposes a multidisciplinary enhancement both in aerodynamics and longitudinal autonomous flight performance (LAFP) via modern simultaneous design methodology conducted with a novel morphing idea. In this regard, the main wing of a fixed-wing unmanned aerial vehicle (UAV) is redesigned with wingtips capable of altering its taper ratio which results in a semi-tapered planform. The dynamic model of morphing aircraft is constituted from data obtained by numerical and analytical approaches for a number of morphing scenarios. The LAFP is identified as the sum of trajectory tracking parameters which are rise time, settling time, and maximum overshoot, while aerodynamic performance is the lift-to-drag ratio. A hierarchically structured control system is designed and the proportional-integral-differential (PID) controller coefficients and the taper ratio of the morphing wingtip are optimized via the Simultaneous Perturbation Stochastic Ap-proximation (SPSA) algorithm. The k-nearest neighbor (k-NN) machine learning algorithm is also conducted to expand the data limited within the investigated range of morphing scenarios so as to have higher accuracy in optimization. Finally, flight simulations of the morphing UAV with optimal wing and control system design are carried out, closed-loop responses are examined in the presence of the von-Karman turbulence model, and the obtained satisfactory results are presented for both disciplines.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"58 41","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot Sentiment Analysis Based on Adaptive Prompt Learning and Contrastive Learning 基于自适应提示学习和对比学习的少量情感分析

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.34021

Cong Shi, Rui Zhai, Yalin Song, Junyang Yu, Han Li, Yingqi Wang, Longge Wang

{"title":"Few-shot Sentiment Analysis Based on Adaptive Prompt Learning and Contrastive Learning","authors":"Cong Shi, Rui Zhai, Yalin Song, Junyang Yu, Han Li, Yingqi Wang, Longge Wang","doi":"10.5755/j01.itc.52.4.34021","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.34021","url":null,"abstract":"Traditional deep learning-based strategies for sentiment analysis rely heavily on large-scale labeled datasets for model training, but these methods become less effective when dealing with small-scale datasets. Fine-tuning large pre-trained models on small datasets is currently the most commonly adopted approach to tackle this issue. Recently, prompt-based learning has gained significant attention as a promising research area. Although prompt-based learning has the potential to address data scarcity problems by utilizing prompts to reformulate downstream tasks, the current prompt-based methods for few-shot sentiment analysis are still considered inefficient. To tackle this challenge, an adaptive prompt-based learning method is proposed, which includes two aspects. Firstly, an adaptive prompting construction strategy is proposed, which can capture the semantic information of texts by utilizing a dot-product attention structure, improving the quality of the prompt templates. Secondly, contrastive learning is applied to the implicit word vectors obtained twice during the training stage to alleviate over-fitting in few-shot learning processes. This improves the model’s generalization ability by achieving data enhancement while keeping the semantic information of input sentences unchanged. Experimental results on the ERPSTMT datasets of FewCLUE demonstrate that the proposed method have great ability to construct suitable adaptive prompts and outperforms the state-of-the-art baselines.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"44 43","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on the Prediction Model of Chinese Tax Revenue Based on GM(1,1) and LSSVM 基于 GM(1,1) 和 LSSVM 的中国税收收入预测模型研究

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.32693

Dan Zhang, Shaoxin Zheng, Wanchun Fu

{"title":"Research on the Prediction Model of Chinese Tax Revenue Based on GM(1,1) and LSSVM","authors":"Dan Zhang, Shaoxin Zheng, Wanchun Fu","doi":"10.5755/j01.itc.52.4.32693","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.32693","url":null,"abstract":"Abstract: In view of the complex influencing factors of tax revenue, the highly non-linear relationship among the influencing factors and the difficulty in predicting tax revenue, this paper proposes to use GM (1, 1) Combined with LSSVM, and it calculates the tax forecasting of China. This paper selects the proportion of the first industry, the ratio of import and export trade to GDP, GDP, the number of urban employment population, the proportion of residents' disposable income and tax revenue in fiscal revenue as the influencing factors, and uses GM (1, 1) and LSSVM respectively to predict the tax revenue of our country, establishes the quadratic programming model to determine the optimal combination weight for the formation of the combination predicting model of tax revenue in our country, make an empirical analysis with the tax revenue of our country from 2000 to 2018 as the research object, and compare the prediction results with LSSVM model, GM (1,1) model and improved GM (1,1) model. The results show that the prediction model of China's tax revenue based on GM (1,1) and LSSVM has a high fitting accuracy with the test set, which can reflect the complex non-linear relationship between various factors. It is of great significance for the development of prediction on Chinese tax revenue and the formulation of a scientific and effective national financial budget.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"82 13","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chronic Kidney Disease Diagnosis Using Conditional Variational Generative Adversarial Networks and Squirrel Search Algorithm 利用条件变异生成对抗网络和松鼠搜索算法诊断慢性肾病

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.34233

B. M. Brinda, Rajan C

{"title":"Chronic Kidney Disease Diagnosis Using Conditional Variational Generative Adversarial Networks and Squirrel Search Algorithm","authors":"B. M. Brinda, Rajan C","doi":"10.5755/j01.itc.52.4.34233","DOIUrl":"https://doi.org/10.5755/j01.itc.52.4.34233","url":null,"abstract":"Globally, the prevalence of chronic kidney disease (CKD) is steadily increasing. Computer-aided automated diagnostic (CAD) methods play a significant part in predicting CKD. Due to their highly effective classification accuracy, CAD systems like deep learning algorithms are essential in diagnosing diseases. This research creates an innovative categorization model with a metaheuristic algorithm based on the best characteristic selection to diagnose chronic kidney disease. Data with the absence of values were first removed during the pre-processing phase. Then, the optimal assortment of attributes is chosen using the Squirrel Search algorithm, a metaheuristic method that aids in more precise disorder prediction or categorization. Conditional Variational Generative Adversarial Networks were suggested for classification to identify the presence of CKD. Performance measures such as accuracy, precision, recall, and F1 score were evaluated on the benchmark CKD dataset to determine the efficiency of the suggested feature selection-based classifier. According to the experimental findings, the proposed method outperformed existing classification models with accuracy, precision, recall, and F1 score values of 99.2%, 98.4%, 98.6%, and 98.9%, respectively.","PeriodicalId":54982,"journal":{"name":"Information Technology and Control","volume":"36 6","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pepper Target Recognition and Detection Based on Improved YOLO v4 基于改进型 YOLO v4 的辣椒目标识别与检测

IF 1.1 4区计算机科学

Information Technology and Control Pub Date : 2023-12-22 DOI: 10.5755/j01.itc.52.4.34183

Zhiyuan Tan, Bin Chen, Liying Sun, Huimin Xu, Kun Zhang, Feng Chen

引用次数: 0