Cognitive Computation and Systems最新文献_第3页

A nonlinear model predictive control based control method to quadrotor landing on moving platform 基于非线性模型预测控制的四旋翼机动平台着陆控制方法

Cognitive Computation and Systems Pub Date : 2023-06-20 DOI: 10.1049/ccs2.12081

Bingtao Zhu, BingJun Zhang, Quanbo Ge

{"title":"A nonlinear model predictive control based control method to quadrotor landing on moving platform","authors":"Bingtao Zhu, BingJun Zhang, Quanbo Ge","doi":"10.1049/ccs2.12081","DOIUrl":"https://doi.org/10.1049/ccs2.12081","url":null,"abstract":"To address the problems that the UAV (Unmanned Aerial Vehicle) is vulnerable to distance limitation and environmental interference when tracking and landing on a moving platform autonomously, the accuracy of position estimation relying only on visual odometry in the point-featureless environment is insufficient, and the traditional linear path planning solvers and controllers cannot meet the fast and safe requirements under the non-linear strong coupling characteristics of the cooperative landing system, an nonlinear model predictive control (NMPC)-based multi-sensor fusion method for autonomous landing of UAVs on motion platforms is proposed. The UAV combines the position information obtained by the RTK-GPS and the image information obtained by the camera and uses the special identification codes placed in the landing area of the UAV to carry out cooperative planning and navigation while using UKF (Unscented Kalman Filter) to estimate the position of the moving platform and using the interference-resistant NMPC algorithm to optimise the UAV tracking trajectory based on the precise positioning of the two platforms to achieve the autonomous landing control of the UAV. The simulation and practical experimental results show the feasibility and effectiveness of the proposed algorithm and the autonomous landing control method and provide an effective solution for the autonomous landing of quadrotors on arbitrarily moving platforms.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 2","pages":"118-131"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50138999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A nonlinear model predictive control based control method to quadrotor landing on moving platform 基于非线性模型预测控制的四旋翼动平台着陆控制方法

Cognitive Computation and Systems Pub Date : 2023-06-20 DOI: 10.1049/ccs2.12081

Bingtao Zhu, BingJun Zhang, Quanbo Ge

引用次数: 0

Out-of-distribution detection based on multi-classifiers 基于多分类器的分布外检测

Cognitive Computation and Systems Pub Date : 2023-06-17 DOI: 10.1049/ccs2.12079

Weijie Jiang, Yuanlong Yu

引用次数: 0

Out-of-distribution detection based on multi-classifiers 基于多分类器的分布外检测

Cognitive Computation and Systems Pub Date : 2023-06-17 DOI: 10.1049/ccs2.12079

Weijie Jiang, Yuanlong Yu

{"title":"Out-of-distribution detection based on multi-classifiers","authors":"Weijie Jiang, Yuanlong Yu","doi":"10.1049/ccs2.12079","DOIUrl":"https://doi.org/10.1049/ccs2.12079","url":null,"abstract":"Existing out-of-distribution detection models rely on the prediction of a single classifier and are sensitive to classifier bias, making it difficult to discriminate similar feature out-of-distribution data. This article proposed a multi-classifier-based model and two strategies to enhance the performance of the model. The model first trains several different base classifiers and obtains the predictions of the test data on each base classifier, then uses cross-entropy to calculate the dispersion between these predictions, and finally uses the dispersion as a metric to identify the out-of-distribution data. A large scatter implies inconsistency in the predictions of the base classifier, and the greater the probability of belonging to the out-of-distribution data. The first strategy is applied in the training process of the model to increase the difference between base classifiers by using various scales of Label smoothing regularisation. The second strategy is applied to the inference process of the model by changing the mean and variance of the activations in the neural network to perturb the inference results of the test data. These two strategies can effectively amplify the discrepancy in the dispersion of the in-distribution and out-of-distribution data. The experimental results show that the method in this article can effectively improve the performance of the model in the detection of different types of out-of-distribution data, improve the robustness of deep neural networks (DNN) in the face of unknown classes, and promote the application of DNN in systems and engineering with high security requirements.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 2","pages":"95-108"},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A shape-based clustering algorithm and its application to load data 一种基于形状的聚类算法及其在加载数据中的应用

Cognitive Computation and Systems Pub Date : 2023-06-10 DOI: 10.1049/ccs2.12080

N. Li, Xian Wu, Jianjun Dong, Dan Zhang

引用次数: 0

A shape-based clustering algorithm and its application to load data 一种基于形状的聚类算法及其在数据加载中的应用

Cognitive Computation and Systems Pub Date : 2023-06-10 DOI: 10.1049/ccs2.12080

Naiwen Li, Xian Wu, Jianjun Dong, Dan Zhang

引用次数: 0

Outlier detection based energy efficient and reliable routing protocol using deep learning algorithm 基于离群点检测的高效可靠路由协议，采用深度学习算法

Cognitive Computation and Systems Pub Date : 2023-06-01 DOI: 10.1049/ccs2.12083

P. J. Lizy, Natarasan Chenthalir Indra

{"title":"Outlier detection based energy efficient and reliable routing protocol using deep learning algorithm","authors":"P. J. Lizy, Natarasan Chenthalir Indra","doi":"10.1049/ccs2.12083","DOIUrl":"https://doi.org/10.1049/ccs2.12083","url":null,"abstract":"Wireless sensor network have also played a vital role in the observation and management of agricultural land in terms of climate, water usage, crops, etc. Due to the open communication system and low battery power of sensors, the agricultural sector still faces issues with energy consumption, information forwarding, and privacy. Thus, an energy‐efficient routing during transmission in WSN‐based smart agriculture is suggested in this study applying a feed‐forward neural network to detect outliers. Outlier identification, CH‐selection, and Relay Node (RN) selection are the three phases of this suggested method. Outlier detection is performed in the deployed nodes for categorises attack nodes from the normal nodes. CH‐selection is performed using a chaotic moth‐flame optimization technique according to distance, node degree, centrality factor and residual energy level, these parameters determine which node will become a Cluster Head. Then reliable routing protocol is designed using NB‐based probability method for RN selection. MATLAB software is used to test the proposed Outlier Detection based Energy Efficient and Reliable Routing Protocol and verify its performance. The effectiveness of the proposed‐model is tested with some prior wireless sensor network routing protocols environment‐fusion multipath routing protocol, dynamic Multi‐hop Energy Efficient Routing Protocol, SEMantic CLustering, and Reliable and energy efficient routing protocol. Outlier Detection based Energy Efficient and Reliable Routing Protocol algorithm attained a 0.91 (%)Packet Delivery ratio, 0.08% of packet loss, 0.91% of Average residual energy, 2.8 (Mbps) throughput, and 26 (sec) Delay.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"127 1","pages":"138-152"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57691973","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}

引用次数: 0

Abnormal event detection model using an improved ResNet101 in context aware surveillance system 基于改进的ResNet101的环境感知监控系统异常事件检测模型

Cognitive Computation and Systems Pub Date : 2023-06-01 DOI: 10.1049/ccs2.12084

Rakesh Kalshetty, A.Vajitha Parveen

{"title":"Abnormal event detection model using an improved ResNet101 in context aware surveillance system","authors":"Rakesh Kalshetty, A.Vajitha Parveen","doi":"10.1049/ccs2.12084","DOIUrl":"https://doi.org/10.1049/ccs2.12084","url":null,"abstract":"Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"153-167"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57692010","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}

引用次数: 0

Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey 创伤性脑损伤后情绪和行为变化的检测：一项综合调查

Cognitive Computation and Systems Pub Date : 2023-03-07 DOI: 10.1049/ccs2.12075

Neha Vutakuri

{"title":"Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey","authors":"Neha Vutakuri","doi":"10.1049/ccs2.12075","DOIUrl":"https://doi.org/10.1049/ccs2.12075","url":null,"abstract":"Traumatic brain injury (TBI) can affect normal brain function and may be caused by a vehicle accident, falling, and so on. The purpose of this survey is to provide clear knowledge of TBI, the causes of TBI, the impacts of TBI, and the role of family members and friends in recovery. TBI affects the daily life of the patients, both physically and mentally. After TBI, the patients may experience many emotional and behavioural changes because of a lack of certain brain functions. These changes affect their personal and social relationships. On the other hand, these changes depend on the severity of the TBI (i.e. mild, moderate, or severe), which is measured using the Glasgow coma score. Generally, three processes are used for emotion recognition: preprocessing, feature extraction, and emotion recognition. Preprocessing is performed for landmark detection and pose normalisation, which improves the performance of emotion detection. Feature extraction and emotion recognition are performed by various deep learning techniques, such as convolution neural networks and long short-term memory. These techniques recognise the behavioural and emotional changes (depression, anxiety, anger, personality changes etc.) of TBI patients using facial expressions. Family members and friends play an important role in TBI patients' recovery, the extent of which is based on the severity of the TBI. The care of family members and friends leads to quick recovery and rehabilitation of patients from TBI. Finally, testing is performed using Computed Tomography images, Magnetic Resonance Imaging images, Electroencephalography signals, and patient demographics, which together show that the deep learning methods achieve better performance in terms of accuracy, precision, recall, and F-measure in recognising emotional and behavioural changes after TBI. The authors conclude with a summary of the future of emotional and behavioural change prediction methods for TBI patients.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"42-63"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50137433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of pedestrians and vehicles in autonomous driving with selective kernel networks 基于选择性核网络的自动驾驶中行人和车辆检测

Cognitive Computation and Systems Pub Date : 2023-03-03 DOI: 10.1049/ccs2.12078

Zhenlin Zhang, Gao Hanwen, Xingang Wu

{"title":"Detection of pedestrians and vehicles in autonomous driving with selective kernel networks","authors":"Zhenlin Zhang, Gao Hanwen, Xingang Wu","doi":"10.1049/ccs2.12078","DOIUrl":"https://doi.org/10.1049/ccs2.12078","url":null,"abstract":"Accurate detection of pedestrians and vehicles on the road is an important content in autonomous driving technology. In this article, a method to optimise the object detection network using the channel attention mechanism is proposed. In general, small object detection problems and difficult sample detection problems in object detection tasks can be solved by using feature pyramids. Different from building a feature pyramid, the authors did not make extensive changes to the network, but used the channel attention mechanism to dynamically adjust the output of a layer during the feature extraction process, allowing each neuron to adjust its receptive field size adaptively according to multiple scales of the input information, so that the network pays attention to the extraction of important features, especially the features of small objects and difficult samples. In order to evaluate the performance of the proposed method, experiments were conducted on standard benchmark data sets. It has been observed that the proposed method is superior to the original object detection network in terms of the detection accuracy of pedestrians and vehicles, especially the detection of small objects.","PeriodicalId":33652,"journal":{"name":"Cognitive Computation and Systems","volume":"5 1","pages":"64-70"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ccs2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0