Computer Sciences Commons^™

Digital Water Depth Processing For Multibeam Based On Average Slope Of Terrain, Jiacheng Yu, Xueqiang Xu

Journal of System Simulation

Abstract: According to the characteristic of multibeam bathymetry in which measurement depth is influenced by terrain, revision algorithm was proposed based on average slope along the horizontal and vertical terrain. Five depth revision factors were obtained, and a depth smooth template was designed. The initial scan depth data were revised by the 5 revision factors, and then the revised depth data were filtered by the template. Simulation results show that, the depth of measurement error can be reduced greatly after revision, the designed template relative to the conventional Gauss template has higher smooth precision, more adapted to the water …

Decoupling Control Research Of Neutral Buoyancy Plant Based On Cmac, Peilong Li, Wang Lei, Gaofeng Li

Journal of System Simulation

Abstract: To solve the control problem of neutral buoyancy plant, a dynamic decoupling and multidimensional compound control agorithm was proposed. By the plant force situation analysis, its six degree motion control model was developed. Then a dynamic decoupling neural network was designed with the merit of adaptation of the dynamic changing coupling situation. CMAC (Cerebellar Model Articulation Controller) and PID compound control was adopted in single channel control, whose advantage is that it combines the rapidity of CMAC feedforward control and anti-interference of PID feedback control. MATLAB simulation results show that the algorithm can control the plant's position and posture …

Simulation Study Of Nodes Deployment Of Underwater Sensor Network Based On Reliability Coverage Algorithm, Juwei Zhang, Yachuang Liu

Journal of System Simulation

Abstract: For 3D nodes-deployment of the underwater sensor networks, the definitions of detection reliability and reliability coverage was proposed. According to the advanced D-S theory of evidence, the change of reliability coverage in underwater sensor networks' detection area was analyzed, and a Nodes-deployment Algorithm Based on Advanced D-S Theory of Evidence and Prior Probability (NAAEP) combining with the prior probability of target nodes distribution was proposed. The value of the target perceived reliability was transformed into a virtual potential field force that a node was suffered in size, and the position of the nodes was adjusted according to …

Simulation Research On Airborne Lidar Bathymetry System, Chengqun Fu, Xiuyuan Lü, Wang Yong, Huaixiao Wang

Journal of System Simulation

Abstract: The simulations were performed to study the Airborne Lidar Bathymetry System by application of Monte Carlo model and Phenomenological model to describe the transmission of Gauss laser beam in ocean water. Research on the relation between seawater dielectric attenuation coefficient and maximum detecting depth was conducted. The change of facular size with detecting depth was explored. Simulation results show that two models are appropriate for different instance. Part of the Airborne Lidar's parameters was analyzed by the two models. According to simulation results, the Airborne Lidar scheme is demonstrated to be feasible.

Energy Entropy And Particle Swarm Optimization Bp Neural Network Of Fault Diagnosis Techniques Of Coal Mine Cable, Zhiling Ren, Yuanyuan Zhang

Journal of System Simulation

Abstract: Aimed at solving the problem of the type of fault difficult to identification when power feeder of coal mine occurred single-phase ground fault, in order to ensure coal mines production safety, a method of fault diagnosis based on wavelet packet energy entropy (WP-EE) and combined with particle swarm optimization neural network was proposed. The type of cable fault was simulated by Matlab, the acquired post-fault voltage signal was performed the three layers wavelet Packet decomposition, the fault characteristic signals was divided into eight segments by frequency, characteristics calculated the entropy energy spectrum according to the information entropy theory, …

Study On Equipment Domain Ontology Construction For Integration Of Data Semantics, Li Kang, Xinming Li, Liu Dong

Journal of System Simulation

Abstract: Equipment domain ontology could accurately describe definition of concepts in the field and the relationships between the concepts, which is foundation and prerequisite in semantic integration of equipment data. With the analysis of the existing methods, a semi-automatic domain ontology construction method was proposed. The top-level domain ontology was built under the standard of authoritative documents in the equipment field. An initial partial ontology was generated from the equipment database by mapping. The ontology was standardized through mapping process. The results of the experiment show that this method can build equipment domain ontology which provides a unified global …

Design Of Iaas Mode “Cloud Training” System, Zhijia Chen, Yuanchang Zhu, Yanqiang Di, Shaochong Feng

Journal of System Simulation

Abstract: There are some problems in equipment web simulating training, including poor sense of reality, low efficiency of training and high difficulty of management. To solve those problems, Infrastructure as a Service mode “cloud training” was proposed. The architecture and operation flow were explained in detail and the key technologies of “cloud training” were focused on. By GPU virtualization technology, the problem of client 3D image processing capability was solved. According to the characters of simulation training, the user requirement model was established. Fuzzy algorithm was introduced to realize resource dynamic scheduling. To assure the stability and reliability, …

Clustering Algorithm Of Quantum Self-Organization Network Based On Bloch Spherical Rotation, Shuyun Yang, Panchi Li

Journal of System Simulation

Abstract: To enhance the clustering ability of self-origanization network, a quantum-inspired self-organization clustering algorithm was proposed based on Bloch spherical rotation. The clustering samples were mapped to the qubits on the Bloch sphere by taking all the sample values as the phases of the qubits, and the all weight values in the competitive layer were mapped to the qubits randomly distributed on the Bloch sphere. Then, the winning node was obtained by computing the spherical distance between sample and weight value, and the weight values of the winning nodes and its neighborhood were updated by rotating them to the sample …

Decision Strategy Models Of Merge Influence Area For Outside Vehicles Based On Vehicle-Vehicle Communication, Xiaofang Yang, Guo Qian, Fu Qiang

Journal of System Simulation

Abstract: In the on-ramp merging area, appropriate driving decisions should be made by observing the running environment and estimating the driving characteristics of the surrounding vehicles, which usually led to the inordinate traffic flow. To solve this problem, the decision strategy models were built for vehicles in the on-ramp merging area, and the operating rules for the new decision strategy were updated. Furthermore, the space-time trajectory, the lane changing in merging and upstream area and the average speed in the two different circumstances were analyzed by numerical experiments. The results show that in the condition of vehicle-vehicle communication environment, …

Study On Simulation Of Turning Burr Formation Based On Finite Element Method, Yunming Zhu, Jingui Huang, Guicheng Wang, Chen Yun

Journal of System Simulation

Abstract: According to the relative motion of tool and workpiece, a new mechanical-thermal coupling finite element model of turning burr formation was established. It adopted supporting plate method to eliminate the influence of workpiece deformation which remained on the workpiece side. Operational efficiency improved greatly under this model. Based on the analysis of simulation results, it is discovered that there are both bending deflection and partly cutting occurred on cutting layer material. AISI1045 steel turning experiment was conducted, it is found that the results of simulation are close to the experimental. The model provides an effective way for analyzing the …

Research Of Marshalling Yard Dispersion Based On Simulation In Container Terminal, Yu Hang, Le Meilong

Journal of System Simulation

Abstract: As development of the precision management in the container terminal, close attention was paid to the research on the optimization of the marshalling yard template. The new concept called the yard dispersion in the container yard template was proposed and the professional simulation software Flexsim-CT was used to build a container handling simulation model which includes berth, quay crane, yard scheduling, yard crane, and trucks. The impact of the different yard dispersion on the total handling time and the efficiency of the quay crane under varying service burden was analyzed. The case study combines the simulation model with …

Optimization Design For A New Type Throttle Valve Of Managed Pressure Drilling Based On Response Surface Methodology, Guorong Wang, Chu Fei, Hongkang Fan, Siyu Tao, Zhu Hao, Jiang Long

Journal of System Simulation

Abstract: The Kriging response surface of single parameter and double parameters versus mass loss of valve spool about bonnet length, bonnet external radius and chamber inner radius of a new type throttle valve used in Managed Pressure Drilling was carried out by using CFX and ANSYS-Design Exploration. The results show that these dimensions have significant influence on erosion mass loss of valve spool. In order to improve the anti-erosion ability of the spool and enhance the service life of the whole valve, the multi-objective genetic algorithm was used for dimensional optimization design of the mentioned variables. After optimization …

Behavior Recognition Combining Regional Optical Flow Features And Temporal Templates, Manyi Wang, Yaling Song, Li Yu, Zhang Liang

Journal of System Simulation

Abstract: A method was proposed to recognize human behavior, which combined the wavelet moments of temporal templates and the speed feature by optical flow. The wavelet moments are not only rotation, translation and scale invariance, but also have the multi-scale characteristics of wavelet. The shape feature of the motion history image and motion energy image can be described. To simple motions, it's effectively, but it can't present the speed information. Regional optical flow was calculated to describe the direction and amplitude features of motion by Lucas-Kanade algorithm. It could make up shortcomings of wavelet moment without speed information. Being …

Bus Frequency Optimization: When Waiting Time Matters In User Satisfaction, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

Research Collection School Of Computing and Information Systems

Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization problem which tries to minimize passengers’ average waiting time. However, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services …

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible …

Solar Power Forecasting Using Wavelet Transform And Machine Learning Approaches, Abdullah Nor Azliana

Student Works (2020-2029)

Generation of photovoltaic (PV) power is intermittent in nature and integration of PV system into the grid system causes an imbalanced power production and power demand. One of the efforts to reduce this problem is to forecast the generation of solar power in the PV system. Solar power forecasting requires the collection of solar power and meteorological data. Hence, this work collected solar power data and various meteorological data (global radiation, tilted radiation, temperature surrounding, humidity surrounding, PV module/ PV panel temperature and wind speed) from Universiti Teknikal Malaysia Melaka (UTeM). A pre-processing process is carried out to ensure that …

A Hybrid Framework Using A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Sabrish Gopalakrishnan, Jianyuan Bo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstrained model that involves parameter tuning. We propose techniques to overcome the challenges in using a QUBO solver that typically comes with limited numbers of bits. First, to smooth the energy landscape, we reduce the magnitudes of the input without compromising optimality. We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce …

Querying Recurrent Convoys Over Trajectory Data, Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M. Choudhury, Hanan Samet

Research Collection School Of Computing and Information Systems

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on …

A Genetic Algorithm To Minimise Number Of Vehicles In An Electric Vehicle Routing Problem, Kiian Leong Bertran Queck, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Electric Vehicles (EVs) and charging infrastructure are starting to become commonplace in major cities around the world. For logistics providers to adopt an EV fleet, there are many factors up for consideration, such as route planning for EVs with limited travel range as well as long-term planning of fleet size. In this paper, we present a genetic algorithm to perform route planning that minimises the number of vehicles required. Specifically, we discuss the challenges on the violations of constraints in the EV routing problem (EVRP) arising from applying genetic algorithm operators. To overcome the challenges, techniques specific to addressing the …

Hybrid Deep Neural Networks For Mining Heterogeneous Data, Xiurui Hou

Dissertations

In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity.

The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and …

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari

Dissertations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …

Towards Practical Homomorphic Encryption And Efficient Implementation, Gyana R. Sahu

Dissertations

Cloud computing has gained significant traction over the past few years and its application continues to soar as evident from its rapid adoption in various industries. One of the major challenges involved in cloud computing services is the security of sensitive information as cloud servers have been often found to be vulnerable to snooping by malicious adversaries. Such data privacy concerns can be addressed to a greater extent by enforcing cryptographic measures. Fully homomorphic encryption (FHE), a special form of public key encryption has emerged as a primary tool in deploying such cryptographic security assurances without sacrificing many of the …

Analyzing And Assisting Patient Decision-Making In Online Health Communities, Mingda Li

Dissertations

In recent years, many users have joined online health communities (OHC) to seek information, suggestions and social support. However, little has been studied about the roles of OHCs in patients' decision-making processes. The aim of this research is to analyze OHC data to better understand patient decision-making processes, and to provide assistance to OHC users in their decision-making.

In order to analyze or assist patients in their decision-making, a novel classification model is designed to identify discussion threads in OHC that are related to decision making. This is achieved by building a two-step combined deep learning model. Empirical evaluation shows …

Ranking Volatility In Building Energy Consumption Using Ensemble Learning And Information Entropy, Kunal Sharma, Jung-Ho Lewe

Georgia Journal of Science

Given the rise in building energy consumption and demand worldwide, energy inefficiency detection has become extremely important. A significant portion of the energy used in commercial buildings is wasted as a result of poor maintenance, degradation or improperly controlled equipment. Most facilities employ sensors to track energy consumption across multiple buildings. Smart fault detection and diagnostic systems use various anomaly detection techniques to discover point anomalies in consumption. While these systems work reasonably well in detecting equipment anomalies over short-term intervals, further exploration is needed in finding methods that consider long-term consumption to detect anomalous buildings. This paper presents a …

Comparison Of Machine Learning Models: Gesture Recognition Using A Multimodal Wrist Orthosis For Tetraplegics, Charlie Martin

The Journal of Purdue Undergraduate Research

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden …

A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi

Engineering Faculty Articles and Research

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

An Effective Method For Synthesizing The Abbreviated Disjunctive Normal Form Of A Boolean Function, Erkin Urunbaev

Scientific Journal of Samarkand University

In discrete mathematics, minimizing Boolean functions in the class of disjunctive normal forms is one of the necessary tasks. This paper presents an effective method for synthesizing the reduced disjunctive normal form of a Boolean function.

Paralleling Simulation Of Operation Plan Based On Decision Point Controlling, Zhanguang Cao, Pinggang Yu, Kuo Wang

Journal of System Simulation

Abstract: Operation plan simulation traditionally uses serial and static method. It can't be fulfilled for the military need of swift simulation and real-time decision in the modern complicated and dynamically battle field. So, the higher complexity and dynamical decision according to situation of the operation plan bring new challenge to the simulation. Operation Plan Paralleling Simulation Based on Decision Point Controlling (P2SDPC) can realize the operation plan's dynamic adjusting and cutting impossible branch based on decision point controlling technology. From then on, the efficiency of operation plan's simulation could be improved by the way of paralleling simulation. Thus the problem …