Risk assessment of autonomous vehicle based on six-dimensional semantic space by Yanan CHEN, Ang LI, Dan WU

“…To address the problems of inadequate extraction of risk elements and low robustness of risk scenario assessment in autonomous vehicles, a risk assessment framework based on six-dimensional semantic space was proposed, which included risk element extraction based on six-dimensional semantic space and risk scenario assessment based on knowledge graph.Formerly, the semantic space was constructed with RGB and IR data mapped, and rich features were extracted using inter-modal correlations for explicit and potential risk elements.Subsequently, risk elements were distilled into a knowledge graph by semantic role annotation and entity fusion, and an inference method was designed by combining node completion and risk level function for accurate risk assessment.Simulations show that the proposed method surpasses current MSMatch and iSQRT-COV-Net in accuracy, false/missed alarm rate, and processing time.…”
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Analysis of Hybrid Spectrum Sensing in Cognitive Radio Using Hybrid Approaches by Haribhau Ashok Shinde, Sandeep Garg

“…A novel hybrid MFD method was developed and evaluated via MATLAB simulations, analyzing factors like sample size, signal-to-noise ratio (SNR), and false alarm probability. Results reveal that ED has a higher miss-detection rate compared to MFD and the proposed hybrid method, which performs particularly well under low sample counts and SNR conditions. …”
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Support Detection for SAR Tomographic Reconstructions from Compressive Measurements by Alessandra Budillon, Gilda Schirinzi

“…In this paper, a support detection method, based on a Generalized Likelihood Ratio Test (Sup-GLRT), is proposed and compared with the SequOMP method, in terms of probability of detection achievable with a given probability of false alarm and for different numbers of measurements.…”
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Knowledge graph based ubiquitous power IoT security visualization technology by Yang YU, Shaomin ZHU, Chaoyi BIAN

“…With the construction of the ubiquitous power IoT,the power network transform to interconnection,the data become more interactive and shared,and the business transform to lateral link-up,which pose new challenges to the original network security protection system.Network security visualization technology helps network security personnel quickly identify potential attacks,locate abnormal events,discover new types of attacks,and quickly capture global network security situation by displaying and analyzing the graph patterns.Based on the knowledge graph,the ubiquitous power IoT security analysis was carried out.The internal and external threat intelligence was modeled firstly.Then the relationship between the conceptual entities was constructed.The threat intelligence as knowledge graph was modeled,and then the abstract and complex alarm information which was invisible was converted into a more intuitive and convenient style,which provided accurate support for ubiquitous network security protection decisions in the power IoT.…”
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Research on the multiuser MIMO linear cooperative spectrum sensing in cognitive radio networks by Yong-qiang HEI, Xiao-hui LI, Wen-tao LI

“…The multiuser MIMO based linear cooperative spectrum sensing problem was investigated in cognitive radio system to improve the reliability of spectrum sensing.Both the local spectrum detecting strategy and global spectrum detecting strategy for multiuser MIMO based linear cooperative spectrum sensing system were derived.Then the optimization model that the different weights assigned on the receive signals of each user at the fusion center for global decision were optimized to maximize the detection probability given a targeted probability of false alarm is established.Furthermore,genetic algorithm (GA) was introduced to find the optimal weight vector of the above-mentioned cooperative spectrum sensing problem,with the purpose of reducing the sensing time in the spectrum sensing process.The simulation results show that,the reliability of spectrum sensing in cooperative spectrum sensing system can be efficiently enhanced with multiple antennas.Besides,the proposed GA method is efficient and stable,and achieves better detection performance when compared with the existing methods.…”
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Communication,navigation and IoT integration 5G indoor communication network by Xinwang ZHANG, Jia DONG, Sichen ZOU, Dong WANG, Wei DENG, Haiyu DING

“…In the 4G era,operators have become data service pipelines,while they will actively seek business transformation in the 5G era.According to research institutes,the indoor positioning and IoT market have broad prospects,and operators are working hard to expand vertical industry applications in related fields.However,the existing indoor communication network has a single function and cannot effectively support indoor positioning and IoT services.A communication,navigation and IoT integration 5G indoor communication network was proposed.By integrating the bluetooth module inside the indoor antenna,it achieved some new functions,such as downlink bluetooth positioning,advertisement information push,path loss detection,uplink IoT collection and uplink bluetooth positioning.The proposed technical solution can be applied to smart medical scenarios,such as smart consultation,equipment management,security management,logistical support and one-click alarm.…”
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Vehicle anti-collision algorithm based on vehicle to vehicle communication by Yourong CHEN, Jinhao WAN, Junjie CHEN, Tiaojuan REN

“…In order to improve the accuracy of predicting vehicle collision accidents and reduce the probability of collision accidents,vehicle anti-collision algorithm（VACA）based on vehicle to vehicle communication was proposed.According to Beidou location coordinates of vehicle itself and other vehicles in the vicinity,VACA used Kalman prediction algorithm to predict next vertical and horizontal locations of vehicles.VACA used data prejudge method to directly judge whether collision will happen.When it could't be directly judged,safe distance with variable acceleration was calculated,and collision state prediction model was established and solved,then minimum collision prediction time could be obtained.If the minimum time was less than threshold value,alarm signal would be sent to remind driver.Simulation results show that compared with IVCWM and ECWA,VACA can predict collision accident more accurately and has certain application value.…”
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Two-level feature selection method based on SVM for intrusion detection by Xiao-nian WU, Xiao-jin PENG, Yu-yang YANG, Kun FANG

“…To select optimized features for intrusion detection，a two-level feature selection method based on support vector machine was proposed.This method set an evaluation index named feature evaluation value for feature selection，which was the ratio of the detection rate and false alarm rate.Firstly，this method filtrated noise and irrelevant features to reduce the feature dimension respectively by Fisher score and information gain in the filtration mode.Then，a crossing feature subset was obtained based on the above two filtered feature sets.And combining support vector machine，the sequential backward selection algorithm in the wrapper mode was used to select the optimal feature subset from the crossing feature subset.The simulation test results show that，the better classification performance is obtained according to the selected optimal feature subset，and the modeling time and testing time of the system are reduced effectively.…”
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Detection of 3D Spatial-Temporal Spectrum Opportunity in Satellite-Terrestrial Integrated Network by Ning YANG, Daoxing GUO

“…The detection of 3D spectrum opportunities in the downlink sharing scenario in satellite-terrestrial integrated networks was investigated.First, defined the 3D space-time spectrum opportunities in the satellite-terrestrial integrated networks, and further divided the opportunities into three areas in the spatial domain: the communication area of primary user, the communication protection belt, and the free access area.Then, based on the proposed 3D opportunity model, derived the closed expressions of the detection and false alarm probability at the user-level and the network-level, respectively.In order to further utilized the spatial spectrum opportunity, a power control scheme of the cognitive user was determined through 3D spectrum sensing, i.e., interference constraint (IC).The simulation results demonstrated that the proposed power control scheme could realized the detection of 3D spectrum opportunity.…”
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Opportunistic spectrum access method for cognitive radio sensor network of typical deployment strategies by Wen-xiang LI, Ya-jie MA, Peng KE

“…Explored the opportunistic spectrum access method for cognitive radio sensor networks with regular topology patterns and coverage patterns.The method includes an economic spectrum-and-timeslot-allocation scheme,a sensing-results-reporting scheme and a cooperative spectrum-sensing scheme.A model was proposed for analyzing the blocking and dropping probability for secondary users,the effective reception bandwidth and transmission delay.Numerical analysis shows that Triangle yields the optimal cooperative sensing performance,and square yields the optimal reception bandwidth.In case of complete coverage,Square and triangle lead to less transmission delay,and in case of multiple coverage,square leads to the least transmission delay.The false alarm probability of single node has little impact on transmission delay,and Primary User’s activity may affect delay noticeably.On the other hand,larger ratio of transmission timeslot to sensing timeslot helps to decrease delay.…”
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Self-corrected coefficient smoothing method based network security situation prediction by Hongyu YANG, Xugao ZHANG

“…In order to solve the problem of insufficient accuracy of current network security situation prediction methods,a new network security situation prediction model was proposed based on self-correcting coefficient smoothing.Firstly,a network security assessment quantification method was designed to transform the alarm information into situation real value time series based on the entropy correlation degree.Then,the adaptive solution of the static smoothing coefficient was calculated and the predicted initial value was obtained by using the variable domain space.Finally,based on the error category,the time-changing weighted Markov chain was built to modify the initial network situation prediction result and the prediction accuracy was further raised.The prediction model was tested with LL_DOS_1.0 dataset and the experimental results show that the proposed model has higher adaptability and prediction accuracy for network situation time series.…”
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Behavioural sleep problems in children by Chun Yi Ting, Biju Thomas

“…With regards to sleep enuresis, management includes behavioural modifications, enuresis alarm and desmopressin. Sleep-related movement disorders include sleep-related bruxism and sleep-related rhythmic movements, of which body rocking is the most common. …”
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Carbon Dioxide, a Releaser for Digging Behavior in Solenopsis Geminata (Hymenoptera: Formicidae) by Walter Hangartner

“…At the same time, Blum and Warter (1966) isolated 2-heptanone from Conomyrma pyramica (Roger) and described its function as the releaser of alarm and digging behavior. Spangler (1968) reported that not only whole workers, but also amputated parts as well as larvae and pupae of Pogonomyrmex occidentalis (Cresson) attract workers of this species and release digging behavior. …”
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Fault diagnosis and auto dispatchin of power communication network based on unsupervised clustering and frequent subgraph mining by Jihua WU, Pengyu ZHU, Zichen WU, Bin GU, Tao HONG, Bo GUO, Jing WANG, Jingyu WANG

“…Fault diagnosis is one of the most challenging tasks in power communication.The fault diagnosis based on rules can no longer meet the demand of massive alarms processing.The existing approaches based on the supervised learning need large sets of the labeled data and sufficient time to train models for processing continuous data instead of alarms, which are far behind the feasibility of deployment.As for alarm correlation and fault pattern discovery, a self-learning algorithm based on the density-based clustering and frequent subgraph mining was proposed.A novel approach for automatic fault diagnosis and dispatch were also introduced, which provided the scalable and self-renewing ability and had been deployed to the automatic fault dispatch system.Experiments in the real-world datasets authorized the effectiveness for timely fault discovery and targeted fault dispatch.…”
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Intrusion detection scheme based on neural network in vehicle network by Yi-liang LIU, Ya-li SHI, Hao FENG, Liang-min WANG

“…Vehicle networking intrusion detection solutions (IDS) can be used to confirm the authenticity of the events described in the notice of traffic incidents.The current Vehicle networking IDS frequently use detection scheme based on the consistency of redundant data,to reduce dependence on redundant data,an intrusion detection scheme based on neural network is presented.The program can be described as a lot of traffic event types ,and the integrated use of the back-propagation (BP) and support vector machine (SVM) two learning algorithms.The two algorithms respectively applicable to personal safety driving fast and efficient transportation system with high detection applications.Simulation results and performance analysis show that our scheme has a faster speed intrusion detection,and has a high detection rate and low false alarm rate.…”
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SLA Audit Mechanism of Virtual Machine Memory on Cloud by Panpan Li, Hongli Zhang, Huimin Deng, Zhigang Zhou

“…Then, digital signatures-based Diffie-Hellman key exchange protocol was also proposed to support strategy security exchange and trust alarm. The experimental results indicate that the proposed module can effectively audit VM memory SLA,and also support strong expansibility of cloud tenant customize strategy with low overhead.…”
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Multidimensional detection and dynamic defense method for link flooding attack by Yang WANG, Guangming TANG, Cheng LEI, Dong HAN

“…Aiming at the shortcomings of the existing link flooding attack defense methods,a multi-dimensional index detection algorithm is proposed,which performs multi-dimensional detection on the abnormal forwarding links through the five-dimensional elements of connection length,low-speed ratio of data packets,uniformity of data packet distance,average low-speed ratio of data packets,and change rate of low-speed ratio of data packets,thus effectively solving the problem of high false alarm rate of the existing detection methods.Furthermore,a controller switch dynamic deployment method based on coloring theory is proposed,which solves the problem of difficult to be actually deployed in the actual environment with limited switch variant types existing in the existing defense mitigation mechanisms.Experimental analysis show the feasibility of the proposed method.…”
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Container intrusion detection method based on host system call frequency by Yimu JI, Weidong YANG, Kui LI, Shangdong LIU, Qiang LIU, Sisi SHAO, Shuai YOU, Naijiao HUANG

“…Container technology has become a widely used virtualization technology in cloud platform due to its lightweight virtualization characteristics.However, it shares the kernel with the host, so it has poor security and isolation, and is vulnerable to flood, denial of service, and escape attacks.In order to effectively detect whether the container is attacked or not, an intrusion detection method based on host system call frequency was proposed.This method took advantage of the different frequency of system call between different attack behaviors, collected the system call generated when the container was running, extracted the system call features by combining the sliding window and TF-IDF algorithm, and classified by comparing the feature similarity.The experimental results show that the detection rate of this method can reach 97%, and the false alarm rate is less than 4%.…”
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Cooperative spectrum sensing algorithm based on limiting eigenvalue distribution by Yin MI, Guang-yue LU

“…A novel maximum-minimum eigenvalue (NMME) cooperative spectrum sensing algorithm and threshold decision rule are proposed via analyzing minimum eigenvalue limiting distribution of the covariance matrix of the received signals from multiple cognitive users (CU) by means of latest random matrix theory (RMT).The proposed scheme could not need the prior knowledge of the signal transmitted from primary user (PU) and could effectively overcome the noise uncertainty.At a given probability of false alarm (P<sub>fa</sub>),simulation results show that the proposed scheme can get lower decision threshold and higher probability of detection (P<sub>d</sub>) compared with the original algorithm,and it can also get better detection performance with fewer CU and smaller sample numbers.…”
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FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining by K. R. Seeja, Masoumeh Zareapoor

“…The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.…”
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