检索结果-内蒙古大学图书馆

46th ICT and Electronics Convention, MIPRO 2023

作者： Zelenicic, V. Miletic, M. Raguz, K. Raic Sumina, D. Erceg, I. University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9789532331042

With the significant increase of nonlinear loads in data center power systems, the losses due to the injection of current harmonics have also increased significantly. In order to reduce current harmonics, the use of Shunt Active Power Filters (SAPF) has become widespread. These filters use active components such as IGBT or MOSFET as well as R,L and C passive components. The SAPF power stage consists of a voltage source inverter (VSI) and an LCL filter, which is used to filter the signal at the switching frequency. The paper presents two simulatons of SAPF. The first simulation refers to a filter used only to reduce the current harmonics, while the second simulation uses a filter that also acts on the current reactive component. The control loop consists of a current controller and a PWM. In the first simulation, the grid side current and the converter side current are used as the current feedback signal. In the second simulation, only the grid side current is used as the current feedback signal. The results of two SAPF simulations are compared and the results show the influence of the control methods on the total current distortion as well as on the current ripple. © 2023 MIPRO Croatian Society.

关键词： Active filters

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning Approach for Commodity Market Trading Strategy 1

引用

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Wong, Pei Yuin Moung, Ervin Gubin Farzamnia, Ali Yahya, Farashazillah Obit, Joe Henry Sani, Zaidatol Haslinda Abdullah Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

The apparent phenomena of commodity price fluctuations significantly affect the cost of living. Most current studies utilize datasets collected before the Russo-Ukrainian War and Covid-19. Moreover, many people are focusing on fund investment, exploring avenues such as commodity trading in addition to stocks and forex investments. However, most research for price prediction in commodities does not cover the periods of Covid-19 and the Russo-Ukrainian war. The aim of this project is to develop trading strategy models to predict whether to buy or sell a commodity, and to evaluate the potential rewards and profits. The dataset used contains daily historical prices of various types of commodities from the year 2000 until March 2022. Furthermore, a real-world dataset, specifically the gold trading dataset from Nasdaq, will be used to validate the performance of the best-performing trading models. The algorithms employed are reinforcement learning-based: Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO). Evaluation performance across six rounds of experiments has shown that the A2C model in a forex environment, using 80% of the dataset for training and 20% for testing, achieved the best results, with a Sharpe ratio of 0.63, a Sortino ratio of 1.0, an Omega ratio of 1.24, and a Calmar ratio of 0.55. The best-performing trading models in Objective 2 and Objective 3 are similar but employ different window sizes. Window size specifies the timesteps that will serve as reference points for the trading model to determine the next trade. Different datasets may require different window sizes, an issue that necessitates further refinement. This refinement is crucial as it involves tailoring the window size to align with the unique characteristics and volatility patterns of each dataset, thereby ensuring that the model's predictive accuracy is optimized for varied market conditions and historical trends. In conclusion, the best-performing trading model is the Advan

关键词： Marketplaces

来源：评论

学校读者我要写书评

暂无评论

Online Conversation-Based Social engineering Detection Using Machine Learning 1

引用

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Ulaganathan, Thurairaj A/L R. Moung, Ervin Gubin Farzamnia, Ali Yahya, Farashazillah Sze, Florence Sia Fui Hung, Lai Po Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Social engineering through online conversations can occur via phone calls, Skype, or Google Meet, among others. This paper presents a machine learning-based classifier for detecting scam conversations in various online formats, including live call conversations. However, selecting an appropriate dataset and the optimal vectorization technique for the algorithm remains challenging, and many fraudulent scams remain undetectable in online conversations. Consequently, six experiments were conducted to apply a machine learning classifier, resulting in 108 outcomes. All six experiments demonstrate that different classifiers possess unique strengths and weaknesses when applied to different scenarios. When compared to Doc2Vec, the vectorization techniques of Universal Sentence Encoder yield excellent results. Among various clustering methods, K-Means and the EM algorithm perform exceptionally well. The results reveal that Random Forest and CatBoost classifiers outperform others in terms of accuracy, precision, recall, and F1-score across all cases. These findings can contribute to enhancing the detection of scam attempts in live call conversations, thus helping protect individuals from falling victim to scams. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Automated Sleep Staging Classification System Based on Convolutional Neural Network Using Polysomnography Signals 1

引用

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azrin, Muhd Luqman Bin Farzamnia, Ali Fan, Liau Chung Moung, Ervin Gubin Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Sleep is a crucial bodily process that plays a vital role in maintaining overall health and well-being. When diagnosing and treating sleep disorders, the initial step is sleep staging. However, manual sleep staging by physicians can be complicated, leading to a growing interest in computer-aided sleep stage classification algorithms. In this research, a method was introduced for automatically classifying sleep stages by extracting distinctive representations from single-channel EEG signals. PSG signals are selected exclusively for the project because they directly capture the essential physiological changes needed for sleep staging, ensuring both data relevance and quality. This choice also aligns with the project’s goal of feasibility and computational efficiency while avoiding potential ethical and privacy issues linked to audio and video data. Furthermore, it conforms to established practises in the field, ensuring consistency in benchmarking. A filterbank is applied by dividing the range of the frequency signal into two 15 sub epochs. The activity of the signal within distinct frequency ranges during different sleep stages was fully comprehended by computing the standard deviation as a single characteristic from different frequency subbands of the EEG. These characteristics served as the input for a two-stream convolutional neural network (CNN) that was trained using a two-stage learning methodology for classification. These characteristics served as the input for a two-stream convolutional neural network (CNN) that was trained using a two-stage learning methodology for classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Low-Back Pain Patients Classification Based on sEMG Activation Patterns Detection 1st

Low-Back Pain Patients Classification Based on sEMG Activati...

引用

1st European Congress on Biomedical and Veterinary engineering, BioMedVetMech 2022

作者： Srhoj-Egekher, Vedran Cifrek, Mario Peharec, Stanislav Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Faculty of Medicine University of Rijeka Rijeka Croatia

ISBN: (纸本)9783031422423

Low back pain (LBP) diagnostic challenge to assigning patients into more specific groups has been detected as one of the key obstacles in ensuring successful rehabilitation treatments. Novel approaches treat LBP patients as a non-homogeneous group where a more individualized approach is proposed, thus leading to a need for an effective subgrouping. In this paper, the possibility to differentiate chronic low back patients (CLBP) from LBP patients with radiculopathy (RLBP), by means of surface electromyography (sEMG), was examined. A feature model based on muscle activation patterns was proposed by measuring time distances between consecutive pairs of distinguished myoelectric events. For each sEMG channel, median and 90th percentile values of time distances were calculated. This procedure was applied to six simple raw features: zero crossing (ZC), signal slope change (SSC), Willison amplitude (WAMP), variance (VAR), relative variance difference (RVD), and permutation entropy (PE). The classification differentiation task (CLBP vs. RLBP) was accomplished by employing support vector machines (SVM) and nearest neighbor (kNN) classifier variants. The presented approach demonstrated consistent high-accuracy classification results (0.90) for the given CLBP and RLBP subgrouping. The obtained results suggest that muscle activation patterns are worth further exploring as part of interpretable LBP patients’ subgrouping efforts. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

An Improved Robust Sparse Convex Clustering

引用

Tsinghua Science and Technology 2023年第6期28卷 989-998页

作者： Jinyao Ma Haibin Zhang Shanshan Yang Jiaojiao Jiang Gaidi Li Beijing Institute for Scientific and Engineering Computing Faculty of ScienceBeijing University of TechnologyBeijing 100124China School of Computer Science and Engineering University of New South WalesSydney 2052Australia

Convex clustering,turning clustering into a convex optimization problem,has drawn wide *** overcomes the shortcomings of traditional clustering methods such as K-means,Density-Based Spatial Clustring of Applications with Noise(DBSCAN)and hierarchical clustering that can easily fall into the local optimal ***,convex clustering is vulnerable to the occurrence of outlier features,as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate *** accurately identify outlier features,this paper decomposes data into a clustering structure component and a normalized component that captures outlier *** from existing convex clustering evaluating features with the exact measurement,the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and *** solve the proposed model,we design an efficient algorithm and prove the global convergence of the *** on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.

关键词： convex clustering outlier features block coordinate descent Newton’s method

来源：评论

学校读者我要写书评

暂无评论

Utilizing Color Space Information as Features for Deep Learning for a Heterogeneous Food Recognition System 1

引用

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Shamsu, Izyan Uzma Binti Moung, Ervin Gubin Farzamnia, Ali Rui, Tiong Lin Yahya, Farashazillah Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Food recognition serves as one of the most promising applications of visual object recognition, as it can be used to calculate food calories and analyze people's eating habits and dietary practices for health-care purposes. It forms the primary and most vital step in developing an application capable of providing nutritional recommendations. In this work, the CNN, ResNet, and AlexNet architectures have been proposed, given their popularity among researchers. A merged dataset, comprising Food-101 and UEC-Food256, was employed to evaluate the proposed models. To ascertain the optimal color spaces for food recognition, eight selection criteria were proposed. Among the various color space selection criteria, RGB showed superior performance when used with the Custom CNN model. To enhance performance, an upsampling method was implemented to increase the number of samples in the minority classes. This was achieved by duplicating existing samples using an augmentation process, thereby balancing the merged dataset. The final results suggest that EfficientNetB0, a CNN-based pre-trained model, performs better with the RGB color space, increasing accuracy from 40.48% (Custom CNN) to 73.11% (EfficientNetB0). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

引用

Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

来源：评论

学校读者我要写书评

暂无评论

OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

引用

Multimedia Tools and Applications 2024年第39期83卷 87199-87235页

作者： M, Bhanumathi B, Arthi Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur Chennai603203 India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur Chennai603203 India

Visualizing the behavior of fish variants is the primary significance for obtaining biological insights in the marine ecological system. Several computer vision and machine learning-based approaches are introduced to classify the fish variants, but these require large data sets to provide high classification accuracy. Deep structured models handle these issues, but they need more attention during training time because some useful information is missed during the training process. It makes it possible to classify the fish variants inaccurately. Therefore, an automatic fish species classification system is developed to provide high accuracy and avoid misclassification results. Thus, this research work explores a new fish species classification strategy with the adoption of a hybrid deep learning technique. The acquired images are pre-processed using contrast-limited Adaptive Histogram Equalization (CLAHE) and histogram equalization methods for cleaning and increasing the quality of images. These images are fed to the hybrid classifier known as Optimized Multi-Scale Atrous Convoluted CNN with Self-Guided Residual Attention Network (OMSACC-SGRAN). Here, the parameters of both Multi-scale Atrous Convoluted CNN and Multi-scale Self Guided Residual Attention Network are tuned using a newly recommended Marine Predators-Forensics-based Investigation inspired Algorithm (MP-FBI). The experimental results proved that this approach achieve excellent results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

Development and Evaluation of a University Chatbot Using Deep Learning: A RAG-Based Approach 8th

Development and Evaluation of a University Chatbot Using Dee...

引用

8th International Workshop on Chatbots and Human-Centered AI, CONVERSATIONS 2024

作者： Olawore, Kabir McTear, Michael Bi, Yaxin Faculty of Computing Engineering and the Built Environment Ulster University Belfast Campus Belfast United Kingdom

ISBN: (纸本)9783031880445

In university systems, traditional methods of information retrieval are often found to be inefficient, leading to frustration among students and staff. This paper presents the development and evaluation of a university-specific chatbot that employs the Retrieval-Augmented Generation (RAG) approach to improve the accuracy and relevance of its responses. Unlike conventional chatbots that depend on intent classification and pre-designed system responses and conversation flows, the proposed chatbot integrates Large Language Models (LLMs) with local university data, enhancing its ability to handle complex queries with context-aware responses and dynamically generated conversation flows. The system architecture includes components such as LangChain for orchestration, a vector store for embedding external knowledge, and a user interface developed using Streamlit. Evaluation results demonstrate that the RAG-based chatbot substantially outperforms traditional LLMs, including GPT-3.5, GPT-4 mini, and GPT-4, in terms of answer accuracy and reliability. In this paper we also reflect on the lessons learned during the chatbot’s development and deployment in a real-world university setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：