{"title":"Object Recognition Based on Improved Zernike Moments and SURF","authors":"Lei Zhang, Hengliang Shi, J. Pu","doi":"10.1145/3375998.3376042","DOIUrl":"https://doi.org/10.1145/3375998.3376042","url":null,"abstract":"Since single global or local features can only describe objects partly or unilaterally that may lead to a low recognition rate, object recognition algorithm based on improved Zernike moments and Speeded-up Robust Features (SURF) is proposed. Firstly, the seven improved Zernike moments and SURF descriptor of objects are extracted, and then the two features are fused together with the weights in term of their contribution to the recognition. Secondly, Euclidean distance is calculated to determine the recognition result. Finally, the performance of algorithm is tested by some image data. Experimental results show that the proposed method is robust to scaling transformation, rotation change and noise variation. Compared with the other three ones, the results show that the proposed method has better recognition performance.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392079","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}
Lin Lei, Yin Weibin, D. Yimin, Xu Jieming, Fan Ming, Yin Jun
{"title":"A P-Cycle Protection Algorithm Based on Capacity Balance for Power Optical Communication Multicast Service","authors":"Lin Lei, Yin Weibin, D. Yimin, Xu Jieming, Fan Ming, Yin Jun","doi":"10.1145/3375998.3376035","DOIUrl":"https://doi.org/10.1145/3375998.3376035","url":null,"abstract":"Aiming at the protection problem of single link/node in the existing power optical communication network multicast service, a p-cycle protection algorithm is proposed. The protection algorithm firstly uses the optimized prim algorithm to generate a multicast tree, and then generates a p-cycle to protect the entire multicast tree through a heuristic shortest P-cycle construction algorithm, and uses a capacity balancing mechanism in the simulation of the protection algorithm to reduce the blocking rate. The simulation results show that the proposed algorithm's resource redundancy and network blocking rate is lower than the existing Hamiltonian ring protection algorithm. It has practical significance for improving the survivability and stability of optical communication networks.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121052693","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 Adaptive Front-lighting Systems with the influence of multiple factors","authors":"Liu Shanzhong, Liu Yongbin, Liang Jinhui","doi":"10.1145/3375998.3376007","DOIUrl":"https://doi.org/10.1145/3375998.3376007","url":null,"abstract":"Aiming at the complexity of Adaptive Front-lighting Systems(AFS) modeling, a method of modeling AFS system by training neural network model is proposed. Through the analysis of prior knowledge and actual situation, Two typical working conditions are modeled in horizontal and vertical directions. RBF neural network is created on MATLAB to train and verify the network, and the fuzzy PID control strategy is added to AFS to optimize the performance of system. Simulation results show that the system model established by this method has a good precision, the fuzzy PID controller can greatly reduce the excessive deflection angle of headlamp, so the service life and the working accuracy of the headlamp can be improved.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126850241","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":"Random Forest Prediction with Improved Feature Selection to Shared Bicycle Demand","authors":"Pengcheng Zhao, Xiaolei Zhang, Shibao Sun","doi":"10.1145/3375998.3375999","DOIUrl":"https://doi.org/10.1145/3375998.3375999","url":null,"abstract":"Under the promotion of green travel, shared bicycles are developing rapidly, but urban road space is wasted due to unreasonable planning. In order to accurately place the number of bicycles, this paper predicts the demand for shared bicycles based on factors such as weather, seasonality and temperature and humidity. Faced with the complexity and collinearity of data features, a random forest prediction shared bicycle demand model with improved feature selection is proposed. First, features with collinearity are excluded by partitioning the feature saliency and correlation coefficient values. Then, the data is effectively characterized. Therefore, the upper bound of the generalization error of the algorithm is reduced. The final prediction model improves prediction accuracy. Experiments show that the random forest algorithm with improved feature selection is optimized, which is compared to other regression algorithms in terms of demand prediction accuracy and fitness. The method is effective.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115897335","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 Harmonic Detection Algorithm Based on Magnetic Nanoparticles","authors":"Zhifeng Zhang, Dandan Wang, Yadong Cui, Nana Li","doi":"10.1145/3375998.3376028","DOIUrl":"https://doi.org/10.1145/3375998.3376028","url":null,"abstract":"In the magnetic nanoparticles hyperthermia, the temperature real-time feedback is the key factor for thermal therapy to treat cancer. But the measuring accuracy of harmonic amplitude determines the accuracy of temperature. At present, the traditional method for harmonic amplitude detection, there is low precision, time-consuming and other issues, seriously affect the magnetic nanoparticles hyperthermia in the medical field of application and promotion. In order to overcome the difficulty, this paper proposes a method to extract the amplitude of harmonic signals using the digital average orthogonal algorithm. The above method describes the principle of amplitude measurement of digital averaging orthogonal algorithm, and deeply studies its detection and filtering characteristics. With the operation of filtering the harmonic components, the magnetization of the magnetic nanoparticles was simulated in the temperature range of 310K to 325K. The simulation results show that the amplitude measurement value is compared with the theoretical value, which has higher amplitude measurement accuracy and faster convergence speed.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129844262","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":"Quality Detection of Magnetic Ring Surface Based-on Machine Vision","authors":"Hongyun Zheng, Jianwei Ma","doi":"10.1145/3375998.3376004","DOIUrl":"https://doi.org/10.1145/3375998.3376004","url":null,"abstract":"This article investigates an effective magnetic ring surface quality inspection method by studying the geometric characteristics of the magnetic ring surface. Digital image processing techniques such as median filtering and threshold transformation are used to accurately detect defects on the surface of the magnetic ring in this paper. The least square circle fit arithmetic and a new roundness measurement algorithm are applied to the geometric feature recognition of the magnetic ring surface. It is proved by practice that the method has accurate conclusions and high precision, and can meet the requirements of magnetic ring surface detection.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128309641","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":"Joint Optimization of Server Placement and Content Caching in Mobile Edge Computing Networks","authors":"Zhen Liu, Jiawei Zhang, Jingyan Wu","doi":"10.1145/3375998.3376024","DOIUrl":"https://doi.org/10.1145/3375998.3376024","url":null,"abstract":"To address the severe challenge from the rapid growth of number of downloads of contents via mobile network, it is imperative to develop efficient content caching scheme in mobile edge computing (MEC) networks, which can alleviate the heavy burden on backhaul links and reduce the delay for content delivery. However, the caching performance is highly related to the MEC server placement. In this paper, we jointly optimize the server placement problem and content caching problem in MEC networks. We propose a heuristic algorithm based on Kuhn-Munkres (KM) algorithm and greedy algorithm to minimize the average delay and average bandwidth resource usage. The simulation shows that our proposed algorithm can reduce delay and bandwidth, compared with other schemes.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907923","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":"Dynamic Path Planning of Mobile Robot Based on Improved Ant Colony Optimization Algorithm","authors":"Yang Liu, Jianwei Ma, Shaofei Zang, Yibo Min","doi":"10.1145/3375998.3376018","DOIUrl":"https://doi.org/10.1145/3375998.3376018","url":null,"abstract":"Aiming at the problem that the traditional ant colony algorithm (ACO) has poor solution quality in the dynamic path planning process, this paper proposes an improved ACO. Firstly, the genetic operator fused with the traditional ACO is proposed, and the genetic operation is used to expand the search space of the solution. Secondly, the fitness function is introduced in the traditional ACO and the safety distance is added. The pros and cons of the comprehensive evaluation algorithm planning path. Then, by introducing the optimization operator, the redundant nodes are eliminated and the smoothness is improved. Finally, the path planning simulation experiment is carried out in the grid map. The results show that the proposed algorithm can find a shorter and smoother in the dynamic environment path.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125548499","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}
Deyu Deng, Jiaxing Jason Mai, C. Leung, A. Cuzzocrea
{"title":"Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence","authors":"Deyu Deng, Jiaxing Jason Mai, C. Leung, A. Cuzzocrea","doi":"10.1145/3375998.3376040","DOIUrl":"https://doi.org/10.1145/3375998.3376040","url":null,"abstract":"In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded in these big data in order to transform this information and knowledge into recommendations and actions. In particular, recommendation systems (RecSys or RS)---which are tools that can provide suggestions to users based on various metrics---have been playing an important role in society since the booming of the Internet. Making more accurate predictions can both potentially increase company revenue and enhance user experience. So, it has been a hot topic. More specifically, collaborative filtering (CF) has been a popular technique applied in RS. The key ideas behind most of the CF algorithms are to filter items based on other users' opinions. Since the recommendation process is based on user interactions, one of the challenges is how to prevent shilling attacks (or shilling attack ratings). In this paper, we propose methods to integrate users' rating entropy into collaborative filtering so as to defend shilling attacks and reduce noisy ratings, and thus achieve higher prediction accuracy. Evaluation results show the effectiveness of our cognitive-based hybrid collaborative filtering methods in rating scaling on entropy for defending shilling influence.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129296863","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":"Multi-scale Correlation Filtering Visual Tracking via CNN Features","authors":"Yibo Min, Jianwei Ma, Shaofei Zang, Yang Liu","doi":"10.1145/3375998.3376009","DOIUrl":"https://doi.org/10.1145/3375998.3376009","url":null,"abstract":"In order to meet the requirements of tracking accuracy and speed of visual tracking algorithm, a multi-scale correlation filtering visual tracking algorithm combined with CNN features is proposed. For the challenge of the training for convolutional neural network consumes numerous data and time we exploit an online convolutional neural network training method that extracts the features of the target context through a shallow network layer. Then, the correlation filtering algorithm is applied to the visual tracking of the given target features and the multi-scale search of the optimal response. Finally, the experiment results show that only using two simple network layers to extract the target features as a multi-channel feature of kernel correlation filtering can achieve excellent results.","PeriodicalId":395773,"journal":{"name":"Proceedings of the 2019 8th International Conference on Networks, Communication and Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125715085","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}