Chao Fan, Qingmin Zhang, Kaifa Kang, Lei Tian, Guo Xukai
{"title":"Design and Research of Indoor Lighting Control System Based on the STM32","authors":"Chao Fan, Qingmin Zhang, Kaifa Kang, Lei Tian, Guo Xukai","doi":"10.2478/ijanmc-2022-0004","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0004","url":null,"abstract":"Abstract After the completion of the process of industrialization in the world, a large number of electrified equipment appeared, People's Daily life is also more and more dependent on electricity security, electricity consumption has risen sharply. At present, various lights driven by electricity play a crucial role in our daily life, which is also the main cause of electricity consumption. We often notice that even when the room is empty, or during the day, the lights are still on, which is a waste of electricity. The waste of power resources caused by traditional lighting equipment cannot be ignored. Therefore, intelligent home furnishing equipment is the favored object of people. So here for a kind of indoor lighting control system is designed, in order to meet the lighting needs on the basis of more humane, more importantly, can save power resources. With the rapid application of automation in daily life and the continuous development of computer technology, lighting control has become increasingly intelligent and automated. The control function of the system is realized by the STM32F103 chip, the illumination is automatically detected by the BH1750 module, and the time parameters are recorded and fed back by the DS3231 clock module. The infrared reflection sensor is used to detect whether there are people in the room. When there is no one, the light will be automatically turned off to save energy. If there are people, when the ambient light intensity is lower than the set value, the light will be automatically turned on to ensure the comfort of the ambient brightness. And can use OLED module real-time display indoor light intensity, number of people and working time. This indoor lighting control system can not only automatically turn on the light according to the environment, but also detect whether there is a human body in the current environment and turn on or off the light by itself, which has more practical significance.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiaojiao Ma, Jun-Peng Yu, Hao Yang, Hong Jiang, Wei Li
{"title":"Fine-grained Recognition of Ships Under Complex Sea Conditions","authors":"Jiaojiao Ma, Jun-Peng Yu, Hao Yang, Hong Jiang, Wei Li","doi":"10.2478/ijanmc-2022-0035","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0035","url":null,"abstract":"Abstract For the traditional deep learning cannot solve the fog, coastal background interference, and the difficulty of small ships recognition, a multi-scale deep learning training model is proposed in this paper. Based on Faster R-CNN, this paper uses guided filtering to remove fog, as well as combined with negative sample enhancement learning to train the model, thus solving recognition of ship in complex sea conditions. And with multi-scale training strategy, the multi-scale ship samples are produced and sent to the network for training, so as to solve the problem of small target recognition. The experimental results show that compared with the Faster R-CNN, the precision and recall of our method increase by 6.43% and by 4.68% respectively. It solves the difficulty of ships recognition under complex sea conditions and small ship recognition that cannot be solved by traditional deep learning methods, the trained model has good generalization ability and robustness.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124161119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Commerce Middle Office Management System Based on Springboot","authors":"Hejing Wu","doi":"10.2478/ijanmc-2022-0014","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0014","url":null,"abstract":"Abstract This topic takes Vue framework as the front-end framework of e-commerce middle office management system, uses springboot framework as the construction mode of back-end framework, and uses Java to write function code. [1] The data of the system is stored in MySQL database, which can be provided to the employee end of registered employees and the management end of managers. Employees can complete registration and login through the e-commerce middle office management system, and then view department information, store information Warehouse information and view order information and add, view commodity file information. The system administrator manages employee information, department information, store type information, store information, warehouse information, commodity file information and order information through the management end. Through the realization of the different functions of the above employees and administrators, the normal operation of the system is ensured, and the process involving stores, commodities and orders is monitored, So as to make a complete set of e-commerce middle office management system that can be provided to all kinds of users.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130954734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spectral Efficiency Classification Schemes for Future Network Communications(SECS)","authors":"Zhongsheng Wang, Qingsong Zhang","doi":"10.2478/ijanmc-2022-0008","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0008","url":null,"abstract":"Abstract Future Network is an project created and managed by ISO/IEC. The project has produced technical reports in ISO/IEC TR 29181 series and is in the process setting architectures and protocols. The project is known for its distinctive “clean slate design” approach and works on fundamental structural innovations to allow Future Network deliver its promises. Simultaneously, ISO/IEC Future Network should prepare itself for future breakthrough in SE technology and make plans to adapt Future Network to the fast changing “Post Shannon Era” technological revolutions. Using reference to the mechanism of radio frequency band classifications, this standard classifies the spectral efficiencies of the MCS systems, so as to facilitate the classification, discussion, evaluation and comparison of the efficiency of the spectrum of information systems.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130231886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yufei Wang, Songze Lei, Yonggang Li, Bo Liu, Huan Zuo
{"title":"Score Level Fusion for Iris and Periocular Biometrics Recogniton Based on Deep Learning","authors":"Yufei Wang, Songze Lei, Yonggang Li, Bo Liu, Huan Zuo","doi":"10.2478/ijanmc-2022-0033","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0033","url":null,"abstract":"Abstract Traditional iris recognition has high recognition accuracy and low misrecognition rate. However, in the case of mobile terminal or distance, the image resolution and image quality decrease, and the recognition rate also decreases. To solve the above problems, this article is based on deep learning technology, on the basis of single mode state recognition, from different levels of multimodal integration, the iris and the eyes in the score level fusion recognition research, put forward the adaptive dynamic weighted score fusion method, to determine the weighing values can adaptive algorithm of the modal, without artificial specified, dynamic weighting algorithm more flexible, stronger applicability. Experimental results of casIA-Iris-LAMP and CasIA-Iris-Distance Iris database in Chinese Academy of Sciences show that the proposed fusion algorithm has higher recognition accuracy and better recognition performance than the single mode recognition algorithm and the traditional fractional fusion method, which proves the effectiveness of the algorithm.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116195633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Face Mask Wearing Detection Based on YOLOv5","authors":"Yunshan Xie, Zhi-yi Hu, Jun-Peng Yu","doi":"10.2478/ijanmc-2022-0017","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0017","url":null,"abstract":"Abstract In recent years, COVID-19 has swept the world, and people in crowded public places are usually large. In order to reduce the risk of virus transmission, stop the spread of the epidemic and reduce cross-infection, wearing masks correctly has become an important measure to prevent the virus. Aiming at the time-consuming and laborious situation of wearing masks manually, this paper proposes a mask wearing detection method based on yolov5. The input layer is mainly used for mosaic data enhancement, that is, adaptive anchor box and adaptive image scaling technology; Yolov5 in backbone mainly adopts focus and CSP (cross stage partial) structure; The neck layer adopts spp (spatial pyramid pooling) module and FPN (feature pyramid networks) + pan (pixel aggregation network) structure; The output mainly adopts ciou for the bounding box loss function_Loss is the average index of NMS (non maximum suppression). This method uses 8000 preprocessed images as the data set and trains 200 epochs to get the final model. The algorithm visually displays the training and test results through tensor board, and inputs the pictures captured by the camera into the model to detect whether the face wears a mask. The accuracy, recall and mean accuracy (map) of the algorithm on the test set are 94.8%, 89.0% and 93.5% respectively, which are higher than the detection results of yolov3 and yolov4 algorithms.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126060876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Air Attack Target Threat Assessment Based on Combination Weighting","authors":"Hong Li, Bailin Liu, Rui Song","doi":"10.2478/ijanmc-2022-0020","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0020","url":null,"abstract":"Abstract Threat assessment is an important process of quantifying the threat of enemy attacking targets. It is also one of the main basis for commanders to make control decisions in air defense operations. Target threat assessment needs to obtain a large amount of air attack target information from various reconnaissance equipment and battlefield sensors, fuse these information, and get the ranking of the threat degree of air attack targets to our side. In view of the unbalanced distribution of index weight in threat assessment in air defense operations, a target threat assessment model based on combined weight is proposed in this paper. Firstly, according to the index system of air raid target threat assessment, the subjective and objective weights of the indexes are determined by analytic hierarchy process and critical method respectively, and the combined weights are calculated by multiplication synthesis method; Then the threat ranking of targets is obtained by TOPSIS method; Finally, the model is verified by an example. The simulation results show that the air target threat assessment model is reasonable.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improved Random Forest Fault Diagnosis Model Based on Fault Ratio","authors":"Ziwei Ding, Shunyuan Huang","doi":"10.2478/ijanmc-2022-0019","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0019","url":null,"abstract":"Abstract With the rapid development of information technology, the informatization, integration and complexity of more and more large equipment are increasing day by day, so it is very important to carry out fault diagnosis for such complex equipment. In the traditional way, expert system technology is usually used for fault diagnosis of complex equipment. However, with the increasing of equipment data information, traditional methods cannot solve the fault diagnosis requirements in the case of a large amount of data. Therefore, data-driven fault diagnosis method can solve this problem, The carrier of data-driven fault diagnosis is a large amount of engineering data, and its focus is to explore new methods of fault diagnosis from a large amount of historical data. In this paper, the classical random forest algorithm is selected as the basic model, and aiming at the imbalance of complex equipment data, the improved random forest voting mechanism based on the fault ratio is proposed to optimize the model, which makes the final model diagnosis accuracy more than 95%, and has good application value.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124581318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on Oil Well Data Cleaning System","authors":"Yao Feng, Li Zhao","doi":"10.2478/ijanmc-2022-0026","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0026","url":null,"abstract":"Abstract In the information age, with the continuous development of Internet technology, information data occupies every field of contemporary society. The development of the big data age makes these data more and more prominent. While users read the information they need from these massive data, data quality has also become a concern of users. A large number of data are preprocessed before data analysis, such as some duplicate values, missing values deal with inaccurate and other abnormal data, and filter the data through the data cleaning system to improve the standardization of the data, so as to improve the analysis efficiency of the data, reduce some unnecessary expenses, and save time and effort. The data cleaning system in this paper is implemented based on flash framework. Taking Python as the main language for data cleaning, technical cleaning and standard integration are carried out for some structural problems, duplication problems and missing problems of some different source data. Through the processing of abnormal data, the data quality and data analysis efficiency are greatly improved.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Network Security System Design of Big Data Platform in Tai'an of Health Based on IPV9 Technology","authors":"Hongwen Zhao, Chao Lu, Yuyu Li, G. Li, Guotao Wen","doi":"10.2478/ijanmc-2022-0003","DOIUrl":"https://doi.org/10.2478/ijanmc-2022-0003","url":null,"abstract":"Abstract At present, IPv4 is the core of the Internet. When it was designed, security protection was hardly considered. Therefore, the Internet has many security loopholes, which cause information leakage or even breakdown. Compared with IPv4, IPv6 has been improved in terms of security, but IPv6 packets are not encrypted and verified by default, and the problem of network layer being attacked is still unresolved. So, the Internet based ipv6 is still faces the risk of data being monitored and tampered, which cannot effectively prevent malicious attacks. China Decimal Network Standard working group developed the future network system, which adopts the zero-trust security mechanism of verification before communication. Big Data Platform in Tai’an of Health is responsible for the unified management of all medical and health institutions in the platform, and for the management, communication and maintenance of all data. Therefore, the establishment of the network security system of this platform should pay more attention to effective, scientific and comprehensive requirements. In this paper, the Future Network (IPV9) with independent intellectual property rights in China and its encryption technology are introduced, and the network security system of Big Data Platform in Tai'an of Health is designed, and the corresponding simulation test is carried out, which achieves the expected effect. The design of network security system of Big Data Platform in Tai'an of Health based on IPV9 can play a certain role and reference value in solving network security problems in Big Data Platform of Health.","PeriodicalId":193299,"journal":{"name":"International Journal of Advanced Network, Monitoring and Controls","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114343907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}