Computer Sciences Commons^™

Dynamics Modeling And Online Prediction Of Energy Consumption Of Discrete Manufacturing System, Wei Chen, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the traditional energy consumption modeling methods of discrete manufacturing system being difficult to adapt to the complexity and variability of working conditions, an online dynamic energy consumption modeling method based on real-time data is proposed. The energy consumption affecting factors are determined by analyzing the operation mechanism of the discrete manufacturing system and equipment. An online sequential extreme learning machine algorithm that can dynamically adjust the number of hidden layer nodes is proposed to construct the energy consumption model. The real-time data can update the model quickly. Bernstein's inequality is introduced to improve the model data screening …

Research Progress Of Opponent Modeling Based On Deep Reinforcement Learning, Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang

Journal of System Simulation

Abstract: Deep reinforcement learning is an agent modeling method with both deep learning feature extraction ability and reinforcement learning sequence decision-making ability, which can make up for the depleted non-stationary adaptation, complex feature selection and insufficient state-space representation ability of traditional opponent modeling. The deep reinforcement learning-based opponent modeling methods are divided into two categories, explicit modeling and implicit modeling, and the corresponding theories, models, algorithms and applicable scenarios are sorted out according to the categories. The applications of deep reinforcement learning-based opponent modeling techniques on different fields are introduced. The key problems and future development are summarized to provide …

Adaptive Correction Tracking Algorithm Based On Detector And Locator Fusion, Yecai Guo, Cheng Liu

Journal of System Simulation

Abstract: In order to avoid tracking failure caused by occlusion, rotation and other factors in complex dynamic scenes, an adaptive correction tracking algorithm based on detector and locator fusion is proposed. The locator trains a convolutional neural network (CNN) filter for location estimation by extracting the deep features of target. The CNN filter adds two layers of shallow features to the three layers of the convolution features of original CF2 algorithm, which enhances the extraction of target texture information. The detector calculates the confidence score by extracting histogram of oriented gradient(HOG) feature of target and combining the context information. …

Cross-Domain Text Sentiment Classification Based On Auxiliary Classification Networks, Na Ma, Tingxin Wen, Xu Jia, Xiaohui Li

Journal of System Simulation

Abstract: To align exactly the texts with same sentiment polarities of source and target domains, and to enlarge the feature difference of different sentiment texts as much as possible, a domain adaptation model with weighted adversarial networks is proposed. A new structured classification network consisting of a main classification network and an auxiliary classification network is proposed, in which the main classification network is used to perform supervised learning on the labeled texts of the source domain, and the auxiliary classification network is used to improve the distinguishability of the text features. A calculation method of multiple adversarial network weights …

Trajectory Control Of Crawler Robot Based On Lstm And Smc, Dongyang Liu, Wenwen Zha, Liang Tao, Cheng Zhu, Lichuan Gu, Jun Jiao

Journal of System Simulation

Abstract: Trajectory tracking is an important part of mobile robot control technology and possesses prospect. Highly nonlinear dynamic characteristics are the main obstacles of controller design. A SMC method based on LSTM and quasi-sliding mode is proposed. The kinematics model and dynamics model of the tracked vehicle are given, and the sliding mode control system is established based on the dynamics model. LSTM network based on deep learning method is designed to control and compensate the unknown interference items, reduce the influence of external interference, and reduce the tremor phenomenon by combining the advantages of LSTM network and quasi-sliding …

Multi-Agent Cooperative Combat Simulation In Naval Battlefield With Reinforcement Learning, Ding Shi, Xuefeng Yan, Lina Gong, Jingxuan Zhang, Donghai Guan, Mingqiang Wei

Journal of System Simulation

Abstract: Due to the rapidly-changed situations of future naval battlefields, it is urgent to realize the high-quality combat simulation in naval battlefields based on artificial intelligence to comprehensively optimize and improve the combat effectiveness of our army and defeat the enemy. The collaboration of combat units is the key point and how to realize the balanced decision-making among multiple agents is the first task. Based on decoupling priority experience replay mechanism and attention mechanism, a multi-agent reinforcement learning-based cooperative combat simulation (MARL-CCSA) network is proposed. Based on the expert experience, a multi-scale reward function is designed, on which a naval …

I-Nicemo Enhanced Algorithm Based On Intersection Angel Geometry, Yifan He, Yulin He, Yongda Cai, Zhexue Huang

Journal of System Simulation

Abstract: To exactly determine the number of cluster centers and correctly identify the candidate cluster centers, an I-niceMO enhanced(I-niceMOEn) algorithm based on intersection angel geometry is proposed. As many distributions of intersection angles and distances as possible between observation points and data points are utilized to recognize the candidate cluster centers to avoid the neglection of cluster centers. The spectral clustering algorithm is used to automatically merge the candidate cluster centers according to the eigenvalues of Laplacian matrices. The number of final cluster centers is determined by the number of merged candidate cluster centers. The number of clusters can be …

Bottleneck Drift Fluctuation Analysis Of Discrete Remanufacturing System Under Disturbance, Yongzhang Zhou, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Considering comprehensively the influence of each production process on the bottleneck degree of discrete remanufacturing system, the interval bottleneck index matrix is established by collecting data repeatedly in the observation stage to obtain the comprehensive bottleneck index of equipment, which is used as the identification basis. Aiming at the volatility of bottleneck drift in the uncertain environment of discrete remanufacturing system, based on the interval bottleneck index matrix and comprehensive bottleneck index, a theoretical method of visual dynamic analysis including system sensitivity coefficient, machine sensitivity coefficient and bottleneck drift judgment model is established. The discrete event simulation case is …

Shared Subnet Synthesis And Application Of Object-Oriented Pres Net, Chuanliang Xia, Maibo Guo, Zhuangzhuang Wang, Yan Sun

Journal of System Simulation

Abstract: Focus on embedded system modeling, a solution to obtain a synthesized net via the shared subnet of an extended Petri net is proposed. Object-oriented technology and Petri net-based representation for embedded system (PRES net) are merged to obtain an object-oriented PRES net (OOPRES net). A method of synthesized operation of the shared subnet of OOPRES net is proposed, and the preservation of the liveness and boundedness of synthesized net system is studied. Taking the modeling analysis of intelligent transportation system as an example, the effectiveness of the synthesized method is verified. The method can provide an effective way for …

Construction Technology Of Hand Posture Dataset Based On Virtual Simulation Method, Jiaxin Chen, Guohui Zhou, Jianbai Yang

Journal of System Simulation

Abstract: Hand posture is an important carrier of human-computer interaction, and the acquisition and recognition of posture information largely depends on the hand posture dataset. Existing datasets can be divided into two categories, real datasets and synthetic datasets. As real data is limited by equipment, environment, and other factors, the classification of hand posture is insufficient and the annotation is mixed with a lot of manual errors. The existing synthetic data can solve the data scale problem of real data, but the synthetic hand posture volume is limited and with some unreasonable kinematic postures of which the data form are …

Research On Unmanned Swarm Combat System Adaptive Evolution Model Simulation, Zhiqiang Li, Yuanlong Li, Laixiang Yin, Xiangping Ma

Journal of System Simulation

Abstract: Aiming at the fact that the intelligent unmanned swarm combat system is mainly composed of large-scale combat individuals with limited behavioral capabilities and has limited ability to adapt to the changes of battlefield environment and combat opponents, a learning evolution method combining genetic algorithm and reinforcement learning is proposed to construct an individual-based unmanned bee colony combat system evolution model. To improve the adaptive evolution efficiency of bee colony combat system, an improved genetic algorithm is proposed to improve the learning and evolution speed of bee colony individuals by using individual-specific mutation optimization strategy. Simulation experiment on …

Atmospheric Corrosion Simulation Of Air Conditioning Heat Exchanger In Service Under Marine Environment, Huang Peng, Jun Wang, Li Qi, Zhidong Wu

Journal of System Simulation

Abstract: Aiming at the performance degradation of airconditioner heat exchanger caused by serious corrosion under marine environment, an atmospheric corrosion simulation method is studied to analyze and predict the influence on corrosion conditions of marine environment and working condition of air conditioner heat exchanger. From the acquisition of material parameters, the construction the model and the setting of boundary conditions, the atmospheric corrosion simulation process of air conditioner heat exchanger in service under marine environment is systematically introduced, and a method to verify the accuracy of the simulation model by using an artificially accelerated environmental test chamber is provided. From …

Leveraging Artificial Intelligence And Machine Learning For Enhanced Cybersecurity: A Proposal To Defeat Malware, Emmanuel Boateng

Cybersecurity Undergraduate Research Showcase

Cybersecurity is very crucial in the digital age in order to safeguard the availability, confidentiality, and integrity of data and systems. Mitigation techniques used in the industry include Multi-factor Authentication (MFA), Incident Response Planning (IRP), Security Information and Event Management (SIEM), and Signature-based and Heuristic Detection.

MFA is employed as an additional layer of protection in several sectors to help prevent unauthorized access to sensitive data. IRP is a plan in place to address cybersecurity problems efficiently and expeditiously. SIEM offers real-time analysis and alerts the system of threats and vulnerabilities. Heuristic-based detection relies on detecting anomalies when it comes …

Geophysical Characterization Of Underground Storage In Salt Domes During The Clean Energy Transition, Joses B. Omojola

LSU Master's Theses

Safely transforming geological formations into cost-effective underground storage is critical for the US energy security and global energy transition. Seasonal energy demand requires relatively cheap, impermeable, non-reactive materials for storing vast amounts of natural gas and hydrogen. Due to their low risk of fracturing and leakage, salt formations are ideal for these purposes however, pressure variations during drawdown, differential salt creep, and reactivation of pre-existing fractures along boundary shear zone(s) (BSZ) can be detrimental to salt cavern safety and long-term cavern operations. Several environmental disasters at storage facilities in the US and Europe, have created a need to understand how …

Tc-Net: A Modest & Lightweight Emotion Recognition System Using Temporal Convolution Network, Muhammad Ishaq, Mustaqeem Khan, Soonil Kwon

Computer Vision Faculty Publications

Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines. Speech Emotion Recognition (SER) is one of the critical sources for human evaluation, which is applicable in many real-world applications such as healthcare, call centers, robotics, safety, and virtual reality. This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker's emotional state. The authors designed a Temporal Convolutional Network (TCN) core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network …

On The Accelerated Noise-Tolerant Power Method, Zhiqiang Xu

Machine Learning Faculty Publications

We revisit the acceleration of the noise-tolerant power method for which, despite previous studies, the results remain unsatisfactory as they are either wrong or suboptimal, also lacking generality. In this work, we present a simple yet general and optimal analysis via noise-corrupted Chebyshev polynomials, which allows a larger iteration rank p than the target rank k, requires less noise conditions in a new form, and achieves the optimal iteration complexity (Equation presented) for some q satisfying k ≤ q ≤ p in a certain regime of the momentum parameter. Interestingly, it shows dynamic dependence of the noise tolerance on the …

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

[Archive] Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

The Beginning, Development And Impact Of Chatgpt In The Digital Age, Zhixiao Zhao, Dongbo Wang

Journal of Scientific Information Research

[Purpose/significance]The emergence of ChatGPT has brought significant changes to the whole society, and to this day, its impact is still spreading, Experts, scholars and news media have broadly discussed it. As a major progress in the field of natural language processing, ChatGPT carries too much attention and expectations. As an important battlefield in the field of natural language processing, information resource management should give full play to the advantages of disciplines under this technological change and drive the development of disciplines with technology.[Method/process] Starting from the origin of ChatGPT, this paper introduces the development path of GPT model, and summarizes …

An Intelligent Path For Improving Diversity At Law Firms (Un)Artificially, Rimsha Syeda

Michigan Technology Law Review

Most law firms are struggling when it comes to diversity and inclusion. There are fewer women in law firms compared to men. The majority of lawyers—81%—are White, despite White people making up only about 65% of the law school population. Lawyers of color remain underrepresented with the historic high being only 28.32%. By comparison, 13.4% of the United States population is Black and 5.9% is Asian. The biases that perpetuate this lack of diversity in law firms begin during the hiring process and extend to associate retainment. For example, an applicant’s resume reveals a lot, including the prestige of the …

Artificial Intelligence In Higher Education: The State Of The Field, Helen Crompton, Diane Burke

Teaching & Learning Faculty Publications

This systematic review provides unique findings with an up-to-date examination of artificial intelligence (AI) in higher education (HE) from 2016 to 2022. Using PRISMA principles and protocol, 138 articles were identified for a full examination. Using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The findings of this study show that in 2021 and 2022, publications rose nearly two to three times the number of previous years. With this rapid rise in the number of AIEd HE publications, new trends have emerged. The findings show that research was conducted in six of …

Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun

Research Collection School Of Computing and Information Systems

Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps …

Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi

Dissertations

Although Artificial Intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. It is crucial to understand the reasons for, mechanisms behind, and circumstances under which AI can fail. For instance, a lack of commonsense reasoning can lead to biased or unfair decisions made by Machine Learning (ML) systems. For example, if an ML system is trained on data that is biased or unrepresentative of the real …

Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to …

Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia

Research Collection School Of Computing and Information Systems

Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive …

Interconnectedness And Financial Stability In The Era Of Artificial Intelligence, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This chapter explains AI use cases may amplify some threats to the stability of the financial system. It argues that lack of a consistent approach to AI Governance by financial regulators and limited oversight on some AI models in terms of systemic risks need to be addressed to preserve the stability of the financial system. To illustrate the problem and propose specific policy recommendations, the chapter first explains how the widespread use AI in finance impacts systemic risk in the financial sector. Then, it explains why trustworthiness of AI encompasses not only the typical ethical principles about transparency, explainability, fairness, …

Regulating Artificial Intelligence In International Investment Law, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric …

Engaging Students Through Conversational Chatbots And Digital Content: A Climate Action Perspective, Thomas Menkhoff, Benjamin Gan

Research Collection Lee Kong Chian School Of Business

In this case study, we report experiences deploying a conversational chatbot as a pre-class and post-class engagement tool for undergraduate students enrolled in sustainability-related courses aimed at educating them about the severity of climate change and the importance of climate action by offsetting one’s carbon footprint (e.g, by planting trees or mangroves in SEA). The intitiative supports the university’s sustainability efforts in general and our new sustainability major in particular aimed at helping students to achieve sustainability-related learning outcomes with reference to climate change and climate action (SDG 13), one of the 17 Sustainable Development Goals established by the United …

Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …

Mimusa: Mimicking Human Language Understanding For Fine-Grained Multi-Class Sentiment Analysis, Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular …

Ten Years After Imagenet: A 360° Perspective On Artificial Intelligence, Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall, Issa Khalil, Xiaosong Ma, Husrev Taha Sencar, Ingmar Weber, Michael Wooldridge, Ting Yu

Natural Language Processing Faculty Publications

It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved - provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous …