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Articles 1291 - 1320 of 11511
Full-Text Articles in Entire DC Network
Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li
Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li
Research Collection School Of Computing and Information Systems
Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large …
Mmlu-Prox: A Multilingual Benchmark For Advanced Large Language Model Evaluation, Weihao Xuan, Et. Al.
Mmlu-Prox: A Multilingual Benchmark For Advanced Large Language Model Evaluation, Weihao Xuan, Et. Al.
Research Collection School Of Computing and Information Systems
Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves …
Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen
Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen
Research Collection School Of Computing and Information Systems
Automated audio captioning (AAC) benefits from incorporatingexternal context to interpret complex sounds, but doing so withretrieval-augmented generation (RAG) at inference is sometimesinfeasible due to data availability or incurs significant latency andcomplexity. We propose DistillCaps, a novel training-time frame-work that leverages RAG to guide knowledge distillation for im-proved audio-language alignment, while lessening the relianceon retrieval during inference. In our framework, a RAG-equippedteacher model retrieves relevant textual information (e.g., simi-lar captions) for each audio clip and uses it for training to gener-ate context-enriched captions. Simultaneously, a student model istrained to imitate this teacher, learning to produce high-qualitycaptions from audio alone. We further …
Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool, Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny T.W. Choo, Cyrille Jegourel
Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool, Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny T.W. Choo, Cyrille Jegourel
Research Collection School Of Computing and Information Systems
Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that combines traditional debugging tools with LLM-based techniques to help novice debuggers fix semantic errors while promoting active engagement in the debugging process. We present findings …
Instructors’ Strategies In Creating And Implementing Constructivist Llm-Based Learning Activities, Emily Aurelia, Shun Yi Yeo, Michelle Lui, Effie Lai-Chong Law, Anthony Tang
Instructors’ Strategies In Creating And Implementing Constructivist Llm-Based Learning Activities, Emily Aurelia, Shun Yi Yeo, Michelle Lui, Effie Lai-Chong Law, Anthony Tang
Research Collection School Of Computing and Information Systems
Large language models (LLMs) are increasingly being integrated into educational settings, enabling more adoption of constructivist teaching and learning approaches in classrooms. This paper explores the strategies instructors are currently using to incorporate LLMs into learning activities that align with constructivist principles, which emphasize that learners actively construct their own knowledge. Through interviews with nine instructors who have designed eleven distinct LLM-based activities and using reflexive thematic analysis, this study identifies various types of learning activities with respect to four different aspects of the constructivist learning theory. The strategies employed and challenges faced to foster constructivist student-LLM interaction were also …
Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua
Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education byfostering effective learning through interactive problem-solving. However, many current ITS models do notfully incorporate proactive engagement strategies that optimize educational resources through thoughtfulplanning and assessment. In this work, we propose a novel and practical task of Goal-oriented IntelligentTutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailoredsequence of exercises and evaluations. We introduce a novel graph-based reinforcement learning framework,named Planning-Assessment-Interaction (PAI), to tackle the challenges of goal-oriented policy learningwithin GITS. This framework utilizes cognitive structure information to refine state representation andguide the selection …
Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen
Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
With the growing influence of the internet and information technology, Electrical and Electronic Equipment (EEE) has become a gateway to technological innovations. However, discarded devices, also called e-waste, pose a significant threat to the environment and human health if not properly treated, disposed of, or recycled. In this study, we extend a novel model for the e-waste collection in an urban context: the Heterogeneous VRP with Multiple Time Windows and Stochastic Travel Times (HVRP-MTWSTT). We propose a solution method that employs deep reinforcement learning to guide local search heuristics (DRL-LSH). The contributions of this paper are as follows: (1) HVRP-MTWSTT …
Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang
Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1—an open-source reasoning model—against OpenAI’s GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning …
Predict Social Economic Outcomes By Transferred Knowledge With Satellite Imagery, Yang Tang, Shih-Fen Cheng, Yunqiang Zhu, Yichen Yang, Zhiqiang Zou
Predict Social Economic Outcomes By Transferred Knowledge With Satellite Imagery, Yang Tang, Shih-Fen Cheng, Yunqiang Zhu, Yichen Yang, Zhiqiang Zou
Research Collection School Of Computing and Information Systems
Traditional deep learning methods and econometric models have played a crucial role in the field of data mining, particularly in the prediction of socioeconomic outcomes. However, socio-economic information is unable to be directly extracted from remote sensing data. So, in this paper, we propose a method to leverage transfer learning to predict socioeconomic indicators (outcomes) through satellite imagery. Specifically, we use road network types as a proxy for socioeconomic factors, which is more effective and stable than using nightlight. We have extracted eleven distinct road topological features to generate reasonable road network types. Given the unique characteristics of road networks, …
Intentionframe: A Semi-Structured, Multi-Aspect Framework For Fine-Grained Conversational Intention Understanding, Zailong Tian, Zhuoheng Han, Lizi Liao, Lizi Liao
Intentionframe: A Semi-Structured, Multi-Aspect Framework For Fine-Grained Conversational Intention Understanding, Zailong Tian, Zhuoheng Han, Lizi Liao, Lizi Liao
Research Collection School Of Computing and Information Systems
Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework inspired by psychological and cognitive intention theories, which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. This design not only retains interpretability but also provides LLMs with a rich context to accurately parse and respond to nuanced user inputs. To efficiently scale IntentionFrame annotations, we introduce a Weakly-supervised Reinforced Generation (WeRG) method that leverages a small set of high-quality human annotations …
Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang
Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang
Research Collection School Of Computing and Information Systems
Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailable or computationally expensive to generate. To overcome this limitation, we propose FlexMol, a flexible molecule pre-training framework that learns unified molecular representations while supporting single-modality input. Specifically, inspired by …
Metacan: Improving Generalizability Of Few‑Shot Anomaly Detection With Meta‑Learning, Zhisheng Lv, Jianfeng Zhang, Songlei Jian, Chenlin Huang, Hongguang Zhang, Guansong Pang, Zhong Liu
Metacan: Improving Generalizability Of Few‑Shot Anomaly Detection With Meta‑Learning, Zhisheng Lv, Jianfeng Zhang, Songlei Jian, Chenlin Huang, Hongguang Zhang, Guansong Pang, Zhong Liu
Research Collection School Of Computing and Information Systems
Few-shot Anomaly Detection (AD) for images aims to detect anomalies with few-shot normal samples from the target dataset. It is a crucial task when only few samples can be obtained, and it is challenging since it needs to be generalized to different domains. Existing methods try to enhance the generalizability of AD by incorporating large vision-language models (LVLMs).However, how to transform category semantic information in LVLMs into anomaly information to improve the generalizability of AD remains a challenge facing existing methods.To address the challenge, we propose a few-shot AD method called MetaCAN, a novel category-to-anomaly network trained with AD meta-learning …
International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua
International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua
Research Collection School Of Computing and Information Systems
Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate …
A Fake Friend? Ai Companions Are Exactly That, Seow Hon Tan
A Fake Friend? Ai Companions Are Exactly That, Seow Hon Tan
Research Collection Yong Pung How School Of Law
In a commentary, SMU Associate Professor of Law Tan Seow Hon discussed how AI companions, which promise emotionally intelligent companionship, have blurred the line between human and machine relationships by mimicking empathy, memory, and affection. She suggested that while such technologies may ease loneliness, they risk fostering narcissism, diminishing real human connection, and replacing authentic friendship with comforting illusions that erode the capacity for love and community.
How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan
How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan
Research Collection Lee Kong Chian School Of Business
Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …
Ai Companions And The Lessons Of Family Law, Clare Huntington
Ai Companions And The Lessons Of Family Law, Clare Huntington
Faculty Scholarship
Virtual friends and lovers powered by artificial intelligence are rapidly moving to the center of our emotional and social lives. Millions of people turn to AI companions every day for conversation, romance, sexual intimacy, therapy, and education. AI companionship holds promise, potentially reducing loneliness, supporting people without access to mental health treatment, helping students learn, and offering a judgment-free space for sensitive conversations. But AI companionship also raises significant concerns. The technology's addictiveness may exacerbate loneliness and can undermine human relationships. Therapy bots may prove more harmful than helpful. AI companions can be emotionally abusive. And their access to the …
Quantum Leap: Harnessing Quantum–Ai Synergy For Resilient Supply Chains And Predictive Routing Under Tariff Shocks, Andrew Burnstine, Raouf Ghattas
Quantum Leap: Harnessing Quantum–Ai Synergy For Resilient Supply Chains And Predictive Routing Under Tariff Shocks, Andrew Burnstine, Raouf Ghattas
Faculty and Staff Publications & Presentations
No abstract provided.
Persepsi Mahasiswa Ilmu Perpustakaan Terhadap Penggunaan Perangkat Ai Llm Dalam Pencarian Informasi, Danisya Laila Zahra, Muhamad Prabu Wibowo
Persepsi Mahasiswa Ilmu Perpustakaan Terhadap Penggunaan Perangkat Ai Llm Dalam Pencarian Informasi, Danisya Laila Zahra, Muhamad Prabu Wibowo
Jurnal Ilmu Informasi, Perpustakaan, dan Kearsipan
The increasing use of generative artificial intelligence (AI) among university students is driving changes in the way they seek and manage information, including in academic contexts. ChatGPT and DeepSeek AI are two AI platforms based on Large Language Models (LLMs) that are increasingly utilized as tools to support information seeking processes. This study aims to analyze the preferences of students from the Library and Information Science Program, Faculty of Humanities, Universitas Indonesia (FIB UI), in using these two platforms. The research employs a case study method with a qualitative approach, involving in-depth interviews with ten students. This study explores their …
Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam
Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam
USF Tampa Graduate Theses and Dissertations
According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …
Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik
Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik
Faculty Publications
Advances in ultra‐intense laser technology have increased repetition rates and average power for chirped‐pulse laser systems, which offer a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser‐plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large ∼1.5 million data point synthetic data set for proton acceleration using a physics‐informed analytic model that we …
Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improved Accuracy, Reliability, And Latency, Nazmus Ashrafi
Thesis/ Dissertation Defenses
The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi-agent collaboration and (2) runtime execution of information-based debugging—for …
Shifted Frequency Analysis Hybrid Simulation Algorithm Based On Multi-Rate Asynchronous Coordination, Yankan Song, Libin Wen, Ying Chen, Jinji Xi, Haoyuan Zhang, Li Xiong
Shifted Frequency Analysis Hybrid Simulation Algorithm Based On Multi-Rate Asynchronous Coordination, Yankan Song, Libin Wen, Ying Chen, Jinji Xi, Haoyuan Zhang, Li Xiong
Journal of System Simulation
Abstract: Large-scale AC/DC power systems exhibit complex dynamics across multiple time scales, and existing hybrid simulations suffer from interface delays and frequency losses during multi-rate coordination, compromising accuracy. To address this issue, a multi-rate asynchronous coordination method was proposed to construct hybrid simulations using shifted frequency analysis (SFA). Within the multi-area Thevenin equivalence (MATE) framework, the algorithm introduced an interpolation-based asynchronous coordination mechanism, effectively eliminating interface delays; by extending SFA theory and designing a universal interface model, it achieved lossless data exchange between partitions with different rates and model types. Case studies on an AC/DC test system demonstrate that …
Soft Sensor Modeling Based On Improved Transformer In Dual-Stream Framework, Hao Gu, Jiayu Wang, Weili Xiong
Soft Sensor Modeling Based On Improved Transformer In Dual-Stream Framework, Hao Gu, Jiayu Wang, Weili Xiong
Journal of System Simulation
Abstract: Industrial process information is highly nonlinear and dynamic, with long-term dependencies between data, making it difficult to adequately extract time-series features. To address this issue, an improved Transformer-based soft sensor model in a dual-stream framework was proposed. The data were segmented and expanded. The features were extracted in parallel using a dual-stream structure combining a convolutional neural network with a self-attention mechanism and the improved Transformer model. The dual-stream features were fused for soft sensor regression. Residual connections were further introduced to accelerate the convergence speed of the model, and an orthogonal random features-based improved multi-head attention mechanism was …
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu
Journal of System Simulation
Abstract: To address the low planning efficiency, poor safety, and limited practicability of the RRT algorithm in global path planning within complex three-dimensional environments, which fail to meet the requirements of planning the safe flight path of UAVs, an improved SAC-RRT algorithm was proposed, which fused SAC deep reinforcement learning algorithm and RRT algorithm. A target point bias strategy and a dynamic step size based on the SAC decision-making network were designed to reduce the blindness of RRT. A random point correction process was designed to optimize the position of random points based on actions from the decision network and …
Low-Energy Multi-Robot Path Planning Algorithm Under Hca* Framework, Ning Wang, Jianlin Mao, Dayan Li, Chengyuan Fang, Chengze Qian
Low-Energy Multi-Robot Path Planning Algorithm Under Hca* Framework, Ning Wang, Jianlin Mao, Dayan Li, Chengyuan Fang, Chengze Qian
Journal of System Simulation
Abstract: To address the energy optimization problem in multi-robot path planning, this paper proposed a multi-robot path planning algorithm based on the energy-guided hierarchical cooperative A* (E-HCA*) algorithm. To address the issue of robot oscillations caused by mutual avoidance at bottlenecks and narrow passages in multi-robot systems, a node expansion method with path length as a secondary feature was introduced, and a greedy suppression strategy under the cooperative A* framework was proposed. A differential-drive robot energy consumption model was established, and an energy-guided heuristic function was constructed by integrating energy metrics into the underlying A* algorithm to guide low-energy path …
Multi-Objective Optimization Of Signal Timing At Intersections Considering Tailpipe Emissions, Xinhuan Ding, Huaqing Wang, Xu Dang
Multi-Objective Optimization Of Signal Timing At Intersections Considering Tailpipe Emissions, Xinhuan Ding, Huaqing Wang, Xu Dang
Journal of System Simulation
Abstract: In order to alleviate urban road congestion and improve the traffic and environmental benefits at intersections, a multi-objective timing optimization model with total delay time, total number of stops, capacity, and total tailpipe emission at intersections as optimization objectives was developed. The model incorporated tailpipe emissions into a mathematical optimization model and quantified the mathematical relationship between traffic efficiency indicators and tailpipe emissions by constructing a specific power-based algorithm for measuring total tailpipe emissions. According to the intersection delay time and the number of stops, the total tailpipe emissions could be estimated. Both the NDX crossover operator and the …
Multisource Information Fusion Method For Human Gait Perception, Guiliang Chen, Guowei Liu, Yongchao Li, Chao Cai, Zihao Li, Dong Yang
Multisource Information Fusion Method For Human Gait Perception, Guiliang Chen, Guowei Liu, Yongchao Li, Chao Cai, Zihao Li, Dong Yang
Journal of System Simulation
Abstract: In response to the insufficient gait perception capability during lower limb exoskeleton assistance, a human lower limb gait phase optimization classification model was proposed. A wireless transmission gait information collection system was designed for collecting the required gait phase feature information. Human joint angles were accurately calculated by fusing acceleration and angular velocity information using extended Kalman filtering. Additionally, kernel principal component analysis was applied to reduce dimensionality in conjunction with plantar pressure data. The LSSVM algorithm was employed to classify gait data, and the PSO algorithm was utilized to find the optimal classification parameters. Experimental results demonstrate that …
Optimal Scheduling Of Integrated Energy Systems Considering Source-Load Uncertainty And Linear Carbon Trading, Huaping Zhong, Yubo Fan, Jijun Shui, Danhao Wang, Daogang Peng
Optimal Scheduling Of Integrated Energy Systems Considering Source-Load Uncertainty And Linear Carbon Trading, Huaping Zhong, Yubo Fan, Jijun Shui, Danhao Wang, Daogang Peng
Journal of System Simulation
Abstract: In order to overcome the impact of source-load uncertainty on the scheduling of integrated energy systems (IES) and reflect the flexibility of the carbon trading price with the change in trading volume, an optimal scheduling method for integrated energy systems considering source-load uncertainty and linear carbon trading was proposed. The equipment within the IES was modeled, and nonparametric kernel density estimation was used to obtain the probability density function for each time period, generating the set of scenes through Monte Carlo simulation and calculating the probability of each scene. For the time shift of wind and solar output peaks …
Simulation Of Three-Degree-Of-Freedom Internal Mode Sliding Mode Control For Non-Ideal Single-Inductor Dual-Output Boost Converter, Bingli Liu, Jiarong Wu, Lin Yang, Dinglin Yan
Simulation Of Three-Degree-Of-Freedom Internal Mode Sliding Mode Control For Non-Ideal Single-Inductor Dual-Output Boost Converter, Bingli Liu, Jiarong Wu, Lin Yang, Dinglin Yan
Journal of System Simulation
Abstract: To reduce the cross-interference in the single-inductor dual-output (SIDO) Boost converter and to enhance the output accuracy and stability of the system, the parasitic resistances of the circuit components were considered, and a three-degree-of-freedom internal model sliding mode control strategy was proposed for the non-ideal SIDO Boost converter. An affine nonlinear mathematical model of the non-ideal SIDO Boost converter was established, and the nonlinear system was linearized and decoupled into two linear subsystems based on the differential geometry theory. The linear subsystem was designed as a three-degree-of-freedom internal model controller and a sliding mode controller, respectively. The robustness …
An Improved Virtual Terrain Generation Method Based On Simplex Noise, Bo Shen, Jianqin Zhang, Shuaibao Ma, Zheng Wen
An Improved Virtual Terrain Generation Method Based On Simplex Noise, Bo Shen, Jianqin Zhang, Shuaibao Ma, Zheng Wen
Journal of System Simulation
Abstract: To address the issues of high computational complexity, slow generation speed, and insufficient realism present in traditional virtual terrain generation methods, this study proposed an improved virtual terrain generation method based on Simplex noise. This method leveraged the advantages of Simplex noise, such as high computational efficiency, low hardware overhead, and more natural randomness, to construct a basic terrain template. A fractal algorithm was introduced to enhance the level of terrain details through the superposition of noises with multiple frequencies and amplitudes. With the integration of a turbulence algorithm, random perturbations and complexity were added to further improve the …