Artificial Intelligence and Robotics Commons^™

Research On Experimental Method Of Joint Operation Simulation Based On Human-Machine Hybrid Intelligence, Ma Jun, Jingyu Yang, Wu Xi

Journal of System Simulation

Abstract: In view of the difficulties that the joint operation simulation experiment methods are mainly for guiding equipment evaluation and demonstration, which is difficult to effectively support the research of operation problems, a joint operation simulation experiment method based on human-machine hybrid intelligence is proposed. The classification, generation and accumulation process of the knowledge in joint operation simulation experiment are clarified. Through the detailed descriptions of experimental interaction process, experimental operation process, experimental driving mode, simulation operation mode, supporting system structure, etc., a joint operation simulation experiment framework based on man-machine hybrid intelligence is constructed. It provides a new method …

Optimized-Xgboost Early Warning Of Wind Turbine Generator Front Bearing Fault, Le Wei, Xiaodong Hu, Yin Shi

Journal of System Simulation

Abstract: In order to identify the abnormal running state of the generator in time, a wind turbine generator front bearing fault early warning method based on Bayesian optimized extreme gradient boosting algorithm is proposed. The historical data collected by SCADA (Supervisory Control And Data Acquisition) are preprocessed by effective data preprocessing methods. The temperature prediction model of the front bearing of wind turbine generator is constructed based on the Bayesian-optimized XGBoost (eXtreme Gradient Boosting) algorithm and the fault early warning threshold of the front bearing of the wind turbine generator is determined based on the 3σ criterion. The experimental …

Reusability Analysis And Application For Complex System Simulation Model, Zhu Feng, Yiping Yao, Wenjie Tang, Li Jin

Journal of System Simulation

Abstract: Model reuse is an important way to improve the development efficiency of complex system simulation application. At present, there are few researches on the reusability of simulation model and its impact on the development efficiency of complex system simulation application integration by using qualitative/quantitative methods. A simulation model reusability analysis method is proposed. By introducing the simulation application integration development speedup ratio, the simulation model reusability analysis mathematical model is constructed, and the requirements of model interface, model testing and information description for improving model reusability are analyzed qualitatively. Through two typical case studies, the influence of reusable simulation …

Scheduling Optimization Research Based On Tabu Algorithm For Uncertainty Carrier Aircraft Support, Yangkai Meng, Wang Zheng, Jiali Fan

Journal of System Simulation

Abstract: In order to improve the scheduling stability of ship-borne and reduce the uncertainty risk during the operation, the deck scheduling mathematical model is designed. For the refueling uncertainty, the equipment failure distribution function is established, and the research method of guarantee operation risk factor value and fault opportunity influence factor is proposed. The uncertainty target function calculation model is derived and solved using the improved taboo algorithm to obtain the scheduling optimization scheme. Simulation experimental results verify the effectiveness and feasibility of the proposed research method.

Research On Assocoation Information Mining Of Space Reconnaissance Equipment System Index, Han Chi, Xiong Wei

Journal of System Simulation

Abstract: The system effectiveness and system contribution rate of the Space Reconnaissance Equipment System (SRES) has a large number of mutally associated indicators. How to identify relationships the association, select the key indicators and clarify the assocition between core indicators and system contribution rate are the key of the evaluation of system effectiveness and contribution rate. Through the joint simulation of MATLAB and STK, the underlying index data of SRES is obtained. Based on the Frequent Pattern-Tree (FP-Tree) algorithm, the assocition information is discovered, the redundancy is removed and the type of indicator assocition is determined, and an optimization model …

Research On The Network Of 3d Smoke Flow Super-Resolution Data Generation, Jinlian Du, Shufei Li, Xueyun Jin

Journal of System Simulation

Abstract: Aiming at the problem of low data generation efficiency due to the high complexity of solving the N-S equation of smoke flow field, a deep learning model which can generate high-resolution smoke flow data based on low-resolution smoke flow data solved by N-S equation is explored and designed. Based on the Generative Adversarial Network, the smoke data reconstruction network based on the sub voxel convolution layer is constructed. Considering the fluidity of smoke, time loss based on advection step is introduced into the loss function to realize high-precision smoke simulation. By extending the image super-resolution quality evaluation index, the …

Modeling And Simulation Of Car Following In Fog Based On Cellular Automata, Zhanhong Liu, Xiujian Yang, Xiangji Wu, Huang Zhen

Journal of System Simulation

Abstract: In order to ensure the safety of driving in foggy weather, a microscopic model of car following and lane changing traffic flow is established based on cellular automata. In view of the instability of car following state in foggy weather, dynamic random acceleration is introduced to segment modeling. The simulation accuracy is improved by refining cell size and cell step size and adding position update allowance. The relationship curves of speed and vehicle dispersion with time and the speed density diagram are obtained. The results show that: the vehicle speed fluctuates greatly in medium fog; the dispersion …

Three-Dimensional Visual Dynamic Simulation Of Rice Leaf Cells, Deheng Zhao, Yingding Zhao, Hongyun Yang, Wenlong Yi

Journal of System Simulation

Abstract: To explore the evolution process of rice leaf cell morphology with the help of computer visualization technology, it is necessary to establish the biomechanical relationship between the cell geometry model and its deformation. A dynamic simulation method is proposed for the 3D visualization of leaf cells based on the physical-mechanical properties, in which the cytoskeleton topology is represented by a "half-edge" data structure, and the surface geometries are constructed using a bicubic Bézier surface. The control parameters of a deformed surface are determined through "vertex", "edge" or "face" topological unit query algorithms of the cytoskeleton, and the cell stress-strain …

Research On Simulation Data Mapping For Production Performance Digital Twin, Junfeng Wang, Yufan Zhang, Yaoqi Shao, Shiqi Li

Journal of System Simulation

Abstract: To fully use the real-time data of the physical workshop to realize the accurate mapping of the data to the virtual workshop and drive the simulation and optimization of the manufacturing system, a data mapping method for the digital twin simulation of production performance is proposed. The fusion process of simulation data and production logic model is analyzed from the aspects of simulation data modeling, storage and updating. Focusing on the production performance simulation requirements, the redundancy analysis, outlier analysis and elimination, statistics and fitting of the original production site sample data is preprocessed. The real-time mapping update process …

Modeling And Simulation Of Channel Passage Capacity Based On Cellular Automata, Zongyang Liu, Chunhui Zhou, Junnan Zhao, Jinli Xiao, Langxiong Gan

Journal of System Simulation

Abstract: In order to study the passage capacity of the seaport and explore the service level of the channel, the cellular automata theory is used to discretize and model the main channel of the seaport, and the movement rules of the cell in the channel are processed in sections. In addition, this study focuses on the existence of a traffic "warning zone" in the curved channel section. The ship motion rules of the straight channel section and the curved channel section are respectively constrained, through simulation experiments, the vessel traffic flow of the channel and the maximum ship passing …

Virtual And Real Fusion Experiment Simulation Technology Based On Gesture Interaction, Xuyan Zou, Hanwu He, Yueming Wu, Jingwei Deng

Journal of System Simulation

Abstract: In experimental teaching, some traditional experiments cannot be carried out due to limitations of experimental environment, experimental equipment, and faculty. In order to solve the above problems, the method of constructing virtual and real fusion simulation experiment using depth cameras is explored.. The Aruco labeling algorithm is used to achieve one-to-one registration of the virtual scene and the real scene, and the experimental environment of virtual and real fusion is constructed combining the colour images and depth images collected by the depth camera in real time. In order to achieve the purpose of operating virtual equipment, a gesture interaction …

Preparing School Leaders To Advance Equity In Computer Science Education, Julie Flapan, Jean J. Ryoo, Roxana Hadad, Joel Knudson

Journal of Computer Science Integration

Background and Context: Most large-scale statewide initiatives of the Computer Science for All (CS for All) movement have focused on the classroom level. Critical questions remain about building school and district leadership capacity to support teachers while implementing equitable computer science education that is scalable and sustainable.

Objective: This statewide research-practice partnership, involving university researchers and school leaders from 14 local education agencies (LEA) from district and county offices, addresses the following research question: What do administrators identify as most helpful for understanding issues related to equitable computer science implementation when engaging with a guide and workshop we …

Statistical Potentials For Rna-Protein Interactions Optimized By Cma-Es, Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima, Brooke Lustig

Faculty Research, Scholarly, and Creative Activity

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.

Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University

Perspectives@SMU

Artificial intelligence presents significant opportunities for business – as well as not insignificant threats to humanity – and governance frameworks are urgently needed to create a fair and equitable future under AI

Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland

Publications

A webinar presentation for AeSSA Young Professionals.

Mlcatchup: Automated Update Of Deprecated Machine-Learning Apis In Python, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers …

Target-Guided Emotion-Aware Chat Machine, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng

Research Collection School Of Computing and Information Systems

The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem and proposes a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leveraging target information to generate more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed …

Conquer: Contextual Query-Aware Ranking For Video Corpus Moment Retrieval, Zhijian Hou, Chong-Wah Ngo, W. K. Chan

Research Collection School Of Computing and Information Systems

This paper tackles a recently proposed Video Corpus Moment Retrieval task. This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel CONtextual QUery-awarE Ranking (CONQUER) model for effective moment localization and ranking. CONQUER explores query context for multi-modal fusion and representation learning in two different steps. The first step derives fusion weights for the adaptive combination of multi-modal video content. The second step performs bi-directional attention to tightly couple video and query as a single joint representation for moment localization. As query context is …

Disambiguating Mentions Of Api Methods In Stack Overflow Via Type Scoping, Kien Luong, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is one of the most popular venues for developers to find answers to their API-related questions. However, API mentions in informal text content of Stack Overflow are often ambiguous and thus it could be difficult to find the APIs and learn their usages. Disambiguating these API mentions is not trivial, as an API mention can match with names of APIs from different libraries or even the same one. In this paper, we propose an approach called DATYS to disambiguate API mentions in informal text content of Stack Overflow using type scoping. With type scoping, we consider API methods …

Eargate: Gait-Based User Identification With In-Ear Microphones, Andrea Ferlini, Dong Ma, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of earworn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable’s occlusion effect to reliably detect the user’s gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate …

Solarslam: Battery-Free Loop Closure For Indoor Localisation, Bo Wei, Weitao Xu, Chengwen Luo, Guillaume Zoppi, Dong Ma, Sen Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose SolarSLAM, a batteryfree loop closure method for indoor localisation. Inertial Measurement Unit (IMU) based indoor localisation method has been widely used due to its ubiquity in mobile devices, such as mobile phones, smartwatches and wearable bands. However, it suffers from the unavoidable long term drift. To mitigate the localisation error, many loop closure solutions have been proposed using sophisticated sensors, such as cameras, laser, etc. Despite achieving high-precision localisation performance, these sensors consume a huge amount of energy. Different from those solutions, the proposed SolarSLAM takes advantage of an energy harvesting solar cell as a …

Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, …

Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced …

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …

Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the …

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …

Tensor Pooling-Driven Instance Segmentation Framework For Baggage Threat Recognition, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi

Computer Vision Faculty Publications

Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art …