Third International Seminar on Artificial Intelligence, Networking, and Information Technology最新文献

{"title":"Reversible data hiding in encrypted domain based on color image channel correlation","authors":"Yu Ge, Minqing Zhang, Pan Yang","doi":"10.1117/12.2667346","DOIUrl":"https://doi.org/10.1117/12.2667346","url":null,"abstract":"Nowadays, due to the increasing need for cloud storage and cloud computing services, digital images need to be kept confidential in order to secure the privacy of individuals' information. Since the currently available codomain reversible data masking techniques are based on gray images, this paper introduces a data masking approach using color image channel correlation to achieve codomain reversibility. In the encryption stage, the security and homomorphism of encryption are achieved through XOR encryption and block scrambling. The data hiding processed by making full use of the texture information data of the reference channel to predict the pixel data more accurately, and an adaptive prediction error expansion technique is used to embed the encrypted information into the R, G, and B channels. The experimental results prove that the method has better security and better performance than the existing algorithms.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116974685","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":"A continual encrypted traffic classification algorithm based on WGAN","authors":"Xiuli Ma, Wenbin Zhu, Yanliang Jin, Yuan Gao","doi":"10.1117/12.2667229","DOIUrl":"https://doi.org/10.1117/12.2667229","url":null,"abstract":"With the constant updating of applications and the emergence of various encryption technologies, a large amount of new encrypted network traffic is generated every day. Therefore, it is a challenging task to achieve continual learning of encrypted traffic. Existing encrypted traffic classification techniques can only handle a fixed number of traffic classes, which is not applicable to real network environments. In this paper, we proposed a continual encrypted traffic classification method based on WGAN, called CETC. The method takes advantage of the powerful data generation capabilities of WGAN to model the data distribution of encrypted traffic. When learning from a new traffic class, the samples from the old class is generated by WGAN to train the new classifier. We use the ISCX VPN-nonVPN dataset to test the performance of CETC. Experimental results show that WGAN can generate high-quality samples of encrypted traffic and the accuracy of CETC is higher than 93%. With its efficient and continual learning capability, CETC can be applied to various encrypted traffic detection and management systems.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121049299","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":"A bi-level structured classifier integrating unsupervised and supervised machine learning models","authors":"Yichen Liu, Zitong Zhang, Chunlei Zhang, Kaiwen Zhang","doi":"10.1117/12.2667225","DOIUrl":"https://doi.org/10.1117/12.2667225","url":null,"abstract":"In this paper, we propose a bi-level structured classifier integrating unsupervised and supervised machine learning models, which aims to improve the model's decision-making ability on classification boundaries by dividing the sample subspace to make full use of the multivariate attribute features and spatial structure of the data. The bi-level structured classifier utilizes the unsupervised clustering algorithms for subspace partitioning of sample data in the first layer, and selects the applicable supervised models to learn on the subspace samples in the second layer. We conduct a case study on a lithology dataset from the complex carbonate reservoirs for lithology identification. The classification results indicate that the bi-level integrated classifier (98.77%) is superior to the machine learning models (XGBoost: 97.67 %). And the ability of the bi-level integrated architecture is verified in effectiveness and generalization, and effectively improves the classification performance.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122648282","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":"Nonradiative infrared thermography detection based on artificial intelligence analysis replaces traditional CT detection","authors":"Jiaqi Chen, X. Su, Jingyi Gong, Ruihan Hu","doi":"10.1117/12.2667226","DOIUrl":"https://doi.org/10.1117/12.2667226","url":null,"abstract":"CT examination utilizes computational functions to achieve tomography of the human body based on the basic characteristics of X-rays, thereby unavoidably producing ionizing radiation that can cause damage to the human body. So, it is not applicable to pregnant women and children; Repeated exposure to CT irradiation in a short period of time may cause leukocytosis, fatigue, dizziness, vomiting and other symptoms. In particular, pregnant women, neonates and patients with extreme weakness are more likely to develop malformation, cancers and other adverse effects after exposure to radiation. However, endoscopic examination will induce physical damage to a certain extent, leading to potential risks of inflammation, and its process will cause fear and discomfort to patients, among which children are more likely to show fear than adults. In addition, there are many practical operation problems for endoscopic examination. So, it is not an ideal method. The medical infrared thermal imaging instrument adopts the high-tech infrared detection technology, which has no radiation and does not touch the human body. When the human body is diseased, the heat balance of the diseased part will also be destroyed. The infrared thermal imaging captures this imbalance based on the infrared rays from the human body to form an infrared thermogram, which reflects the temperature characteristics of the human body and thus will not harm the human body. The instrument has now already passed the clinical verification. Infrared thermography can well reflect the presentation of sinusitis, especially performs well in distinguishing whether the inflammation is acute or chronic. And the expression on infrared thermography is better than CT. Combined with artificial intelligence imaging algorithms, it can achieve feature analysis at the level of a single pixel and provide doctors with more detailed and accurate reference data, so as to implement efficient auxiliary diagnosis. The instrument is suitable for various types of hospitals and medical institutions, and even for home medical diagnosis when it is combined with a remote auxiliary diagnosis system.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122873013","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":"Anti-noise kinematic controller for redundant manipulators based on model driven neural network","authors":"Xin Chen, Xin Su","doi":"10.1117/12.2667671","DOIUrl":"https://doi.org/10.1117/12.2667671","url":null,"abstract":"In this paper, a model-based anti-noise neural network controller for redundant robot motion control is proposed for motion control of redundant robots with uncertain kinematic parameters. The main challenge of this problem is the coexistence of parameter uncertainty, redundancy resolution, and system physical constraints. Therefore, a new model - driven neural network controller is proposed in this paper. A class of nodes are introduced to deal with the kinematic parameter uncertainty of the system. On this basis, the selection of the initial value of the hyperparameter of the neural network is deeply analyzed, and this processing has a positive effect on accelerating the convergence of the tracking error. The proposed controller has the advantages of simple structure, small computation and simple implementation. The simulation of Kinova Jaco2 manipulator verifies the effectiveness of the proposed algorithm.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126274455","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":"Intelligent robot navigation system based on data mining algorithm","authors":"Pingchuan Ma, Lichuan Xi","doi":"10.1117/12.2667670","DOIUrl":"https://doi.org/10.1117/12.2667670","url":null,"abstract":"Artificial intelligence, electronics, and computers are developing faster and faster, embedded processors and machinery, intelligent robots have gained widespread attention in the market. The purpose of this paper is to design an intelligent robot navigation system based on data mining algorithm. Firstly, the navigation framework of intelligent robot based on ROS system is introduced. Then the key technologies of navigation are studied, and the path planning algorithm and self-positioning algorithm are introduced respectively. Finally, the robot navigation system is built according to the navigation framework, and the robot fixed-point navigation experiment is completed on the robot platform of this paper. In the navigation accuracy measurement experiment, A, B, C, and D are set as the coordinates of the target points, and each point is tested for navigation. The position error of the two points D in the x direction is about 0.05m, while the coordinate error in the y direction is larger, which is greater than the set 0.05m. The designed system can correctly construct the map of environmental information and can avoid obstacles and move to the set target position accurately and autonomously, which verifies the reliability and accuracy of the experimental platform and the navigation system.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126348947","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":"Normalization method of encryption source in mimicry simulation system","authors":"Bo Zhang, Zesheng Xi, Lei Wang, Chuan He, Kun Cao, Yu-Na Wang","doi":"10.1117/12.2667625","DOIUrl":"https://doi.org/10.1117/12.2667625","url":null,"abstract":"Although the mimic system can effectively defend against known or unknown vulnerabilities / backdoor attacks, some encryption protocols such as SSH will produce different encryption results on different executors, even with the same processor, the same operating system, the same encryption protocol and the same plaintext, which leads to difficulty in output arbitration. To solve this problem, this paper proposes an encryption source normalization method, which can make different executors generate same ciphertext by normalizing the source of the random number and synchronizing the length of output data, so that the output of heterogeneous executers can be successfully arbitrated by the scheduler. This method is verified by experiments using SSH protocol. Test results show that this method can effectively solve the encryption problem of mimic system.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127915708","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":"UAV 3D route planning algorithm based on improved RRT","authors":"Yu Liu, Zi-lv Gu, X. Bai, Bao-guo Wang, Di Wu, Guang-lin Yu","doi":"10.1117/12.2667611","DOIUrl":"https://doi.org/10.1117/12.2667611","url":null,"abstract":"An improved algorithm based on fast extended random tree (RRT) was proposed to solve the 3d route planning problem of uav in complex environment. Firstly, the planning space modeling is carried out according to the threat factors of flight route. Secondly, in view of the large randomness of THE RRT algorithm, the heuristic distance function is introduced as the basis for the selection of nodes to be expanded, so as to increase the probability of nodes near the target being selected as nodes to be expanded, and improve the way of generating new nodes in the random tree to accelerate the convergence speed of the algorithm. Then, UAV dynamics constraints were incorporated into the new node to meet the flight path requirements. Finally, b-spline curve was used to optimize the smoothness of the initial route curvature discontinuity problem. Simulation results show that the improved algorithm has certain advantages in planning speed and route length, and can effectively solve the problem of UAV 3D route planning.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117396","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":"Modeling and optimization design for RF humidity perception in storage environment","authors":"Lihong Wang, Chunhua Zhu","doi":"10.1117/12.2667758","DOIUrl":"https://doi.org/10.1117/12.2667758","url":null,"abstract":"Radio Frequency (RF) perception of humidity is the current research hotspot of grain condition monitoring for \"three temperature and three humidity\". In order to analyze the optimal detection parameters on humidity of storage environment, the Wireless Insite test platform is built to analyze the effects of antenna height, carrier frequency and grain and with different moisture content on Channel State Information (CSI) such as received power, path loss and time delay. The mathematical relations between the humidity and path loss, the humidity and time delay are deduced respectively. In simulation experiments, the frequency bands of 2.4GHz, 5GHz, 28GHz, 45GHz, 60GHz are selected. Study the changes of path loss and delay spread with humidity in the storage environment at different frequencies and antenna height. When the height of the transceiver antenna is set to 1m, the 28GHz, 45GHz and 60GHz millimeter wave signals are more sensitive to humidity changes, thereby which can provide more higher perception resolution of detecting humidity in the storage environment, compared with the 2.4GHz and 5GHz frequency bands. Besides, when space humidity is constant and the moisture content of wheat is changed, the arrival time of each ray in the channel at 45GHz is longer than that at 60GHz. The above work can provide reference for the application of RF sensing technology in the humidity detection scenario.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130876886","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":"Distributed machine learning framework and algorithm implementation in Ps-Lite","authors":"Jinrui Wang, Baorun Chen, Yinghan Du, Yan Feng, Quan Qian","doi":"10.1117/12.2667367","DOIUrl":"https://doi.org/10.1117/12.2667367","url":null,"abstract":"Big data analysis based on artificial intelligence is particularly important in the era of Internet. The data is stored in different regions in industry. Meanwhile, sending data to servers generates huge amount of communication cost for centralized training. The distributed machine learning can resolve the storage of data and decrease the cost of data communication. But different distributed machine learning frameworks are also limited with the problems of low algorithm compatibility and poor expandability. The aim of this paper is building the distributed machine learning framework based on Ps-Lite and implementing algorithms in the framework. The framework is realized with asynchronous communication and computation methods. The algorithm implementation includes gradient-aggregating algorithm (distributed Stochastic Gradient Descent) and three regression algorithms (Logistic Regression, Lasso Regression and Ridge Regression). The algorithm implementation illustrates that common algorithms fit this framework with high compatibility and strong expandability. Finally, the experiment of Logistic Regression implementation proves the performance of the framework. The computation time of unit node is saved 50% with the increase of node number. The accuracy of the training model is maintained above 70% in the framework. The convergence efficiency of Logistic Regression is 3 times higher than that of the traditional one in the multiple-node framework.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131431776","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}