Digital Commons Network^™

Dynamic Stochastic Relay Selection Algorithm In Peer-To-Peer Networks, Chengwei Zhang, Wenqing Cheng, Hei Xiaojun

Journal of System Simulation

Abstract: A stochastic routing algorithm for selecting appropriate relay nodes in peer-to-peer networks was proposed. This algorithm was constructed using a stochastic programming framework by leveraging the actual delay of local links and the statistical delay distributions of non-local overlay links. In order to approximate the statistical delay distribution of non-local overlay links, the historical delay values of each link were utilized to approximate the link delay distribution. This algorithm was fully distributed and the stochastic link measurement data could be updated between neighboring nodes at a longer time granularity to reduce routing overhead. This algorithm and …

Bifurcation Multi-Path Grooming Algorithm In Multi-Granularity Transport Network, Guangwei Lei, Xingwei Wang, Huang Min, Zhao Yue

Journal of System Simulation

Abstract: Recent years, with the rapid development and wide use of multi-granularity transport network, the pressure of backbone transmission network has been alleviated to some extent. However, the growing user demands to the Internet make network power consumption and bandwidth still face severe challenge. To deal with it, a bifurcation multi-path grooming algorithm was proposed and its associated bandwidth allocation strategy was devised to reduce power consumption and blocking rate. The proposed algorithm has been implemented by simulation and the evaluation results show that its performance is better than that of the benchmark.

Research On Modeling Of Pump Model Based On Rbf Neural Network, Qinghui Wu, Qinghuan Shen, Xinjun Wang

Journal of System Simulation

Abstract: On the basis of research on pump characteristics and pump model, a modeling method based on RBF neural network with K-means clustering algorithm was proposed. With k-means clustering algorithm the center vector and base width parameters, in which lie in the hidden layer, were optimized by input data sample. The weights between the hidden layer and the output layer were optimized by input-output data sample with least squares method. The neural network models of the pump characteristic and pump comprehensive model were separately trained and checked using the detected data. Its results suggest that reasonably choosing the number of …

Recent Advances In Finite Element Simulation Of Machining Processes, Caixu Yu, Chunbin Cai, Huang Cui, Erliang Liu

Journal of System Simulation

Abstract: Recently, the finite element simulation technology has been widely applied in machining simulation and has played an irreplaceable role in the investigations of machining mechanism of metal and composite materials, optimization of cutting parameters,etc. The importance of finite element simulation, commonly used software and material constitutive model were introduced. The latest research progress of the finite element simulation of cutting at home and abroad was summarized, including the simulation of chip morphology, the simulation of cutting force and cutting temperature, tool wear simulation, the machined surface integrity, simulation of the effect of multi-steps cutting on cutting process and finite …

Learning To Learn Kernels With Variational Random Features, Xiantong Zhen, Haoliang Sun, Yingjun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek

Machine Learning Faculty Publications

We introduce kernels with random Fourier features in the meta-learning framework for few-shot learning. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTMbased inference network effectively integrates the context information of previous …

Rrsds: Towards A Robot-Ready Spoken Dialogue System, Casey Kennington, Daniele Moro, Lucas Marchand, Jake Carns, David Mcneill

Computer Science Faculty Publications and Presentations

Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.

How To Make Sure That Robot's Behavior Is Human-Like, Vladik Kreinovich, Olga Kosheleva, Laxman Bokati

Departmental Technical Reports (CS)

In many applications -- e.g., in health care -- it is desirable to make robots behave human-like. This means, in particular, that robotic control should not be optimal, it should be similar to human (suboptimal) behavior. People's decisions are based on bounded rationality: since we cannot compute an optimal solution for all possible situations, we divide situations into groups and come up with a solution appropriate for each group. What is optimal here is the division into groups. It is therefore desirable to implement a similar algorithm for robots. To help with such algorithms, we provide techniques that help optimally …

Learning Word Groundings From Humans Facilitated By Robot Emotional Displays, David Mcneill, Casey Kennington

Computer Science Faculty Publications and Presentations

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 …

Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we …

Adaptive Large Neighborhood Search For Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

Cross-docking is considered as a method to manage and control the inventory flow, which is essential in the context of supply chain management. This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD which has been extensively studied due to its ability to reducethe overall costs occurring in a supply chain network. Given a fleet of homogeneous vehicles for delivering a single type of product from suppliers to customers through a cross-dock facility, the objective of VRPCD is to determine the number of vehicles used and the corresponding vehicle routes, such that the vehicleoperational and transportation …

Nonlinear Dimensionality Reduction For The Thermodynamics Of Small Clusters Of Particles, Aditya Dendukuri

Graduate Theses and Dissertations

This work employs tools and methods from computer science to study clusters comprising a small number N of interacting particles, which are of interest in science, engineering, and nanotechnology. Specifically, the thermodynamics of such clusters is studied using techniques from spectral graph theory (SGT) and machine learning (ML). SGT is used to define the structure of the clusters and ML is used on ensembles of cluster configurations to detect state variables that can be used to model the thermodynamic properties of the system. While the most fundamental description of a cluster is in 3N dimensions, i.e., the Cartesian coordinates of …

Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns. We propose a framework by extending GPT-2 models to tackle these challenges by formulating video-grounded dialogue tasks as a sequence-to-sequence task, combining both …

Query Graph Generation For Answering Multi-Hop Complex Questions From Knowledge Bases, Yunshi Lan, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets.

Trajectory Similarity Learning With Auxiliary Supervision And Optimal Matching, Hanyuan Zhang, Xingyu Zhang, Qize Jiang, Baihua Zheng, Zhenbang Sun, Weiwei Sun, Changhu Wang

Research Collection School Of Computing and Information Systems

Trajectory similarity computation is a core problem in the field of trajectory data queries. However, the high time complexity of calculating the trajectory similarity has always been a bottleneck in real-world applications. Learning-based methods can map trajectories into a uniform embedding space to calculate the similarity of two trajectories with embeddings in constant time. In this paper, we propose a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation. We use a simple and fast trajectory simplification and indexing approach to obtain triplet training samples efficiently. We make the framework more robust via taking full …

Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya

Student Works (2020-2029)

Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network …

Unsupervised Monocular Depth Estimation With Multi-Scale Structural Similarity Powered Loss Function, Kohan Ali

Student Works (2020-2029)

Depth Estimation refers to a set of techniques and algorithms that aim to obtain a representation of spatial information of a scene. Nowadays specific hardware such as sensors, radars and multiple-view-recording cameras are being used in order to acquire depth data of a scene. Modern approaches use deep learning to address this task by trying to learn depth information in a supervised manner. However, this approach requires a large amount ground-truth data for a particular scene so that a model can be trained successfully. Also preparing ground-truth data for a range of environments is a challenging and expensive task to …

Efficient Vehicle Routing Optimization For Autistic Users, Mohammed Shabalah Abdulrahman Hasan

Student Works (2020-2029)

In recent years, daily life without a vehicle would be impossible. As an inevitable result, the number of vehicles on the road increases day by day in various large cities around the world. The increased number of vehicles is a big concern because it causes a lot of traffic congestions, especially during peak hours. Besides, there has been a rapid rise of on-demand Ride-Hailing Services (RHSs), such as Grab, Uber, EzCab, and MyCar, etc. This allows passengers with smartphones to place trip requests and assign them to drivers according to requester’s location and drivers' availability. In consequence, efficient routing algorithms …

Evaluating Human Versus Machine Learning Performance In Classifying Research Abstracts, Yeow Chong Goh, Xin Qing Cai, Walter Theseira, Giovanni Ko, Khiam Aik Khor

Research Collection School Of Economics

We study whether humans or machine learning (ML) classification models are better at classifying scientific research abstracts according to a fixed set of discipline groups. We recruit both undergraduate and postgraduate assistants for this task in separate stages, and compare their performance against the support vectors machine ML algorithm at classifying European Research Council Starting Grant project abstracts to their actual evaluation panels, which are organised by discipline groups. On average, ML is more accurate than human classifiers, across a variety of training and test datasets, and across evaluation panels. ML classifiers trained on different training sets are also more …

Next-Generation Self-Organizing Communications Networks: Synergistic Application Of Machine Learning And User-Centric Technologies, Chetana V. Murudkar

USF Tampa Graduate Theses and Dissertations

The telecommunications industry is going through a metamorphic journey where the 5G and 6G technologies will be deeply rooted in the society forever altering how people access and use information. In support of this transformation, this dissertation proposes a fundamental paradigm shift in the design, performance assessment, and optimization of wireless communications networks developing the next-generation self-organizing communications networks with the synergistic application of machine learning and user-centric technologies.

This dissertation gives an overview of the concept of self-organizing networks (SONs), provides insight into the “hot” technology of machine learning (ML), and offers an intuitive understanding of the user-centric (UC) …

Simulation-Based An Exploratory Analysis Of Drone Air Strike On Saudi Oilfields, Jiajun Zhang, Xiaochao Qian, Jinpeng Zhou, Xuanling Wang, Zhifeng Lu

Journal of System Simulation

Abstract: The incident of air strike in Sandi Arabia in September 2019 is taken as an example，A rapid analysis method for crisis events based on simulation is proposed. Combining the simulation with the exploratory analysis, the uncertainty of crisis events is comprehensively analyzed and dealt with. According to the public information of the incidents details from Sandi Arabia, Yemen, the combat situation of the air strike is replayed based on the wargaming. The rationality and authenticity of the details in the event statement is verified and can also provide reference to the development and combat research of the “Low, Small …

Multi-Tank Adaptive Scheduling Algorithm And Simulation For Ship Stability Realization, Xiong Yong, Xuanzhuo Liang, Zhang Jia

Journal of System Simulation

Abstract: Aiming at the influence of the oil tank on the floating state and stability of the ship, the multi-tank adaptive dispatching control algorithm to maintain the stability of the ship is studied. The automatic floating state adjustment simulation system of the ship is realized by the WinCC (Windows Control Center) configuration software. For the system architecture, the Boolean logic table is used to construct the connection relationships of the oil tanks, pumps and pipelines, and the amount of oil exchange between the cabins is calculated according to the floating equation, and the balance of the floating state in the …

Simulation-Based Effectiveness Evaluation For System-Of-System Under Multi-Equipment Collaboration, Ma Ning, Shenglin Lin

Journal of System Simulation

Abstract: Complex coordination exists among the various equipment in the system-of-system (SoS) operational process. The influence relationship among the various indexes is ignored in the traditional effectiveness evaluation methods. Aiming at this, a new effectiveness evaluation approach for the SoS is proposed. Numerous evaluation data is analyzed, based on the association rules mining, to obtain the influence among the effectiveness indexes. The networked evaluation index system is constructed, and the effectiveness integration evaluation is realized based on the analytic network process. The feasibility and validity of the proposed method are verified by evaluating the effectiveness of an air defense SoS.

Identification Method For Fractional-Order Systems Based On Haar Wavelet Operational Matrix, Yuanlu Li, Li Teng, Baoying Liu

Journal of System Simulation

Abstract: Studies have shown that the dynamical systems can be more accurately described by the fractional-order systems because its order can be any number. That’s why the fractional-order systems are being paid more and more attention. However, how to create a fractional-order system is still in the exploratory stage. Considering the nonlocal features of the fractional differentiation, a method for the fractional-order system identification is proposed by taking the Haar wavelet operational matrix. The proposed method can reduce the dimension of the operational matrix by abandoning the high frequency coefficients of the input and output signals so that the buffer …

Classification Of Chest X-Ray Disease Based On Convolutional Neural Network, Huang Xin, Fang Yu, Mengdan Gu

Journal of System Simulation

Abstract: The artificial intelligence technology can effectively assist the chest X-ray diagnosis. On the basis of the analysis of Chinese reports of chest X-rays, a labeling method of the thoracic disease classification for the chest abnormal parts is proposed and a dataset of the thoracic disease classification labels is complied. The thoracic disease classification is evaluated through four kinds of convolutional neural networks, AlexNet, VGGNet, ResNet and DenseNet and through three kinds of training methods, direct training, ImageNet pre-training and Chest X-14 pre-training. The result shows that the more complicated convolutional neural network with the more parameters, the better performance …

Study Of Parallelized Graph Clustering Algorithm Based On Spark, Dongjiang Liu, Jianhui Li

Journal of System Simulation

Abstract: The parallelized graph clustering algorithm is researched. A new parallelized graph clustering algorithm is proposed based on Spark. As the top operation of Spark occupies a lot of memory space, a new algorithm which is used to substitute the top operation is proposed to reduce the memory consumption. By improving bottom up hierarchical clustering algorithm, the speed of the proposed algorithm is improved. A new data filtering method based on the feature of graph data is proposed. By the method, the running time and memory space comsuption is reduced greatly. The reason of the high efficiency of this filtering …

A Multi-Strategy Differential Evolution Algorithm Combined With Neighborhood Search, Can Sun, Xinyu Zhou, Mingwen Wang

Journal of System Simulation

Abstract: The difficulties of designing a multi-strategy differential evolution (DE) algorithm are how to select the mutation strategies and allocate these strategies. A multi-strategy DE algorithm combined with the neighborhood search operator is proposed. The population is divided into three subpopulations according to the fitness values, and each subpopulation employs a different mutation strategy and parameter settings to complement the search ability, to balance the exploration and exploitation ability of the whole population. The subpopulation with the best fitness values employs the neighborhood search operator to exploit possible benefit information to guide the search. Extensive experiments are carried out on …

Holiday Highway Traffic Flow Prediction Method Based On Deep Learning, Xiaofeng Ji, Yicheng Ge

Journal of System Simulation

Abstract: Accurately predicting highway traffic holiday flow can provide important data for the emergency management of highway. The LSTM-SVR prediction model is established by using the theoretical framework of deep learning. The BP neural network is used to process the sample data, and the data features captured by LSTM are input into the SVR regression layer to realize the traffic flow prediction. Before and after the “Eleventh” Golden Week, the LSTM-SVR model was verified by using the traffic monitoring data of the intermodulation station in Lijiang City and the prediction results were compared with the others. It is found that …

Study On Evacuation Cellular Automaton Model Considering Avoidance Behavior, Yinghua Song, Zhang Yu, Feizhou Huo, Lü Wei, Yaping Ma

Journal of System Simulation

Abstract: To study the avoidance behavior in the evacuation process, a cellular automaton model considering avoidance behavior is established. The model divides the evacuees into avoiders and transferees, and reflects the avoidance behavior by changing the selected probability of evacuees. The impact of the avoidance behavior on the efficiency and evacuation process in different personnel ratios and different initial positions is analyzed. The results show that the avoidance behavior can optimize the evacuation sequence, improve the evacuation efficiency and reduce the risk of evacuation process. The evacuation time is significantly reduced when the probability of avoidance exceeds the thread value, …

Crowd Evacuation Model Based On Improved Pso Algorithm, Changhua Li, Chenggong Bi, Zhijie Li

Journal of System Simulation

Abstract: Aiming at the poor local optimization ability and easy premature convergence of the particle swarm optimization (PSO) algorithm in evacuation simulation, a crowd evacuation model based on the improved PSO algorithm is proposed. On the basis of the original model, factors such as the forward driving force, exclusion driving force and panic degree of evacuees are introduced. According to the environmental factors and crowd movement, evacuees' direction is dynamically adjusted, and tends to the ideal exit under the optimal exit selection mechanism. The parametric simulation experiment of evacuation efficiency is carried out for the physical factors such as the …

A Discrete Krill Herd Algorithm For The No-Wait Flow Shop Scheduling Problem, Changping Liu, Zhenfu Jian, Wenhan Fu

Journal of System Simulation

Abstract: A discrete krill herd algorithm (DKH) is proposed to solve the no-wait flow shop scheduling problem (NWFSP) with the makespan criterion. Based on the characteristics of the optimization mechanism, the notion of “virtual food” in krill herd algorithm and the influenced crossover rules between krill individuals are defined. The concept and calculation method of the individual distance are redefined to measure the neighborhood range to realize the information exchange. The local search strategies of random swap, inverse and insert operator are employed to enhance the local search ability of DKH, and the multiple initialization strategies are redesigned to promote …