104 research outputs found

    Fractal assessment analysis of China's air-HSR network integration

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    High-speed rail (HSR) has emerged as a significant mode for intercity transport in several countries, particularly China, setting an environment that may promote integration between air and HSR networks. To better measure the current level of integration of China's air-HSR intermodal network and identify implementation issues, this paper establishes a novel assessment framework that considers three primary areas: service capability, network connectivity and transfer potential. The framework is based on a comprehensive literature review of network measurement and assessment methodologies. Then, fractal theory is used to establish an assessment model that associates the fractal dimension to the level of intermodal integration, which can serve as an important complement to traditional weighting methods. The model and framework are applied to the 10 cities in China with the potential for air-HSR integration. The results show that international hub airports, together with their closest HSR station, do not necessarily perform at a higher integration level than regional hubs. The paper also proposes policy and practical recommendations to enhance air-HSR network integration levels from service supply, network coordination and transfer design perspectives

    Reliability Improvement On Feasibility Study For Selection Of Infrastructure Projects Using Data Mining And Machine Learning

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    With the progressive development of infrastructure construction, conventional analytical methods such as correlation index, quantifying factors, and peer review are no longer satisfactory in support for decision-making of implementing an infrastructure project in the age of big data. This study proposes using a mathematical model named Fuzzy-Neural Comprehensive Evaluation Model (FNCEM) to improve the reliability of the feasibility study of infrastructure projects by using data mining and machine learning. Specifically, the data collection on time-series data, including traffic videos (278 Gigabytes) and historical weather data, uses transportation cameras and online searching, respectively. Meanwhile, the researcher sent out a questionnaire for the collection of the public opinions upon the influencing factors that an infrastructure project may have. Then, this model implements the backpropagation Artificial Neural Network (BP-ANN) algorithm to simulate traffic flows and generate outputs as partial quantitative references for evaluation. The traffic simulation outputs used as partial inputs to the Analytic Hierarchy Process (AHP) based Fuzzy logic module of the system for the determination of the minimum traffic flows that a construction scheme in corresponding feasibility study should meet. This study bases on a real scenario of constructing a railway-crossing facility in a college town. The research results indicated that BP-ANN was well applied to simulate 15-minute small-scale pedestrian and vehicle flow with minimum overall logarithmic mean squared errors (Log-MSE) of 3.80 and 5.09, respectively. Also, AHP-based Fuzzy evaluation significantly decreased the evaluation subjectivity of selecting construction schemes by 62.5%. It concluded that the FNCEM model has strong potentials of enriching the methodology of conducting a feasibility study of the infrastructure project

    Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

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    The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management

    Natural Order: The Case for Applying Biomimetic Design Principles to Mass Communication Technology Design

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    In this paper I tested the effectiveness of a biomimetically designed classifier algorithm in an effort to support a new argument for the systemic application of biomimetic design principles to mass communication technology. To supplement the purely system-level test, I conducted a series of interviews with interface-level designers regarding their own design strategies, generally accepted design strategies in the field of mass communication technology design, new design strategies, and the landscape of the field in general. The findings of my test lend strong credence to biomimicry\u27s potential systemic contribution to mass communication technology design, and the tone of the interview responses suggests that the practices of interface-level design are congruent with this contribution. I argue that the placement of biomimetic design principles at the systemic level would enhance the user-interface design practices already in place, given their congruency with biomimetic design principles. I argue that to improve usability, interactivity, and security, and to improve our consumption, storage, and transmission of information on a massive scale, the most prudent course of action is to concentrate biomimetic design strategies systemically--into our hardware, networks, and systems in general--and that user-interface design would not only accommodate the changes to our system-level designs, but that it would thrive on them

    Spatio-Temporal Information for Action Recognition in Thermal Video Using Deep Learning Model

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    Researchers can evaluate numerous information to ensure automated monitoring due to the widespread use of surveillance cameras in smart cities. For the monitoring of violence or abnormal behaviors in smart cities, schools, hospitals, residences, and other observational domains, an enhanced safety and security system is required to prevent any injuries that might result in ecological, economic and social losses. Automatic detection for prompt actions is vital and may help the respective departments effectively. Based on thermal imaging, several researchers have concentrated on object detection, tracking, and action identification. Few studies have simultaneously extracted spatial-temporal information from a thermal image and utilized it to recognize human actions. This research provides a novelty based on frame-level and spatial and temporal features which combines richer context temporal information to address the issue of poor efficiency and less accuracy in detecting abnormal/violent behavior in thermal monitoring devices. The model can locate (bounded box) video frame areas involving different human activities and recognize (classify) the actions. The dataset on human behavior includes videos captured with infrared cameras in both indoor and outdoor environments. The experimental results using the publicly available benchmark datasets reveal the proposed model\u27s efficiency. Our model achieves 98.5% and 94.85% accuracy on IITR Infrared Action Recognition (IITR-IAR) and Thermal Simulated Fall (TSF) datasets, respectively. In addition, the proposed method may be evaluated in more realistic conditions, such as zooming in and out etc

    Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives

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    This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect 90.4% and 98.7% cyberattacks with an f1-score of 90.0% and 98.5%, respectively. Our second topic proposes a novel QoS prediction model tested in a live 5G network environment. Compared to the existing work in this domain, our study is the first approach to conduct a large-scale field test in a 5G network to measure and forecast the network QoS metrics. More than 50 days of continuous data have been collected, cleaned, and used for training the deep sequence models to predict the 5G network QoS metrics such as throughput, latency, jitter, and packet loss. Our experiments demonstrate the effectiveness of predicting the QoS metrics using LSTM and LSTM Encoder-Decoder models, providing lower prediction errors of 14.57% and 13.75%, respectively

    Resiliency in Deep Convolutional Neural Networks

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    The enormous success and popularity of deep convolutional neural networks for object detection has prompted their deployment in various real-world applications. However, their performance in the presence of hardware faults or damage that could occur in the field has not been studied. This thesis explores the resiliency of six popular network architectures for image classification, AlexNet, VGG16, ResNet, GoogleNet, SqueezeNet and YOLO9000, when subjected to various degrees of failures. We introduce failures in a deep network by dropping a percentage of weights at each layer. We then assess the effects of these failures on classification performance. We find the fitness of the weights and then dropped from least fit to most fit weights. Finally, we determine the ability of the network to self-heal and recover its performance by retraining its healthy portions after partial damage. We try different methods to re-train the healthy portion by varying the optimizer. We also try to find the time and resources required for re-training. We also reduce the number of parameters in GoogleNet, VGG16 to the size of SqueezeNet and re-trained with varying percentage of dataset. This can be used as a network pruning method

    Optimized Ensemble Approach for Multi-model Event Detection in Big data

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    Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately
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