Journal of Intelligent & Fuzzy Systems最新文献_第4页

DEFR-net: A decompose-enhance fourier residual network for fault diagnosis of rotating machine with high noise immunity DEFR-net：用于高抗噪旋转机械故障诊断的分解增强傅立叶残差网络

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-233190

B. Du, Fujiang Zhang, Jun Guo, Xiang Sun

{"title":"DEFR-net: A decompose-enhance fourier residual network for fault diagnosis of rotating machine with high noise immunity","authors":"B. Du, Fujiang Zhang, Jun Guo, Xiang Sun","doi":"10.3233/jifs-233190","DOIUrl":"https://doi.org/10.3233/jifs-233190","url":null,"abstract":"The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"4 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human activities recognition from video images by using convolutional neural network 利用卷积神经网络识别视频图像中的人类活动

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-236068

Dan Wang, Jingfa Yao, Yanmin Zhang

{"title":"Human activities recognition from video images by using convolutional neural network","authors":"Dan Wang, Jingfa Yao, Yanmin Zhang","doi":"10.3233/jifs-236068","DOIUrl":"https://doi.org/10.3233/jifs-236068","url":null,"abstract":"Nowadays, automatic human activity recognition from video images is necessary for monitoring applications and caring for disabled people. The use of surveillance cameras and the processing of the obtained images leads to the achievement of a smart, accurate system for the recognition of human behavior. Since human detection in different scenes is associated with many challenges, several approaches have been implemented to detect human activity from video image processing. Due to the complexity of human activities, background noises and other factors affect the detection. For the solution of these problems, two deep learning-based algorithms have been described in the current article. According to the convolutional neural networks, the LSTM + CNN method and the 3D CNN method have been used to recognize the human activities in the images of the video. Each algorithm is explained and analyzed in detail. The experiments designed in this paper are performed by two datasets: the HMDB-51 dataset and the UCF101 dataset. In the HMDB-51 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 70.2 and for method 3D CNN equal to 54.4. In the UCF101 dataset, the highest obtained accuracy for CNN + LSTM method was equal to 95.1 and for method 3D CNN equal to 90.8.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"25 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Potential functions of construction worker–machine interaction safety assessment 建筑工人与机器互动安全评估的潜在功能

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-236423

Yu Bai, Q. Hu, Zhenxiang Zhou, Q. Cai, Leping He

引用次数: 0

Natural language command parsing for agricultural measurement and control based on AMR and entity recognition 基于 AMR 和实体识别的农业测量和控制自然语言指令解析

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-237280

Weihao Yuan, Mengdao Yang, Hexu Gu, Gaojian Xu

{"title":"Natural language command parsing for agricultural measurement and control based on AMR and entity recognition","authors":"Weihao Yuan, Mengdao Yang, Hexu Gu, Gaojian Xu","doi":"10.3233/jifs-237280","DOIUrl":"https://doi.org/10.3233/jifs-237280","url":null,"abstract":"There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"12 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Landscape image recognition and analysis based on deep learning algorithm 基于深度学习算法的景观图像识别与分析

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-239654

Nong Limei, Dongfan Wu, Zhang Bo

{"title":"Landscape image recognition and analysis based on deep learning algorithm","authors":"Nong Limei, Dongfan Wu, Zhang Bo","doi":"10.3233/jifs-239654","DOIUrl":"https://doi.org/10.3233/jifs-239654","url":null,"abstract":"Garden landscape is the combination of nature and humanity, with high aesthetic value, ecological value and cultural value, has become an important part of people’s life. Modern people have a higher pursuit for the spiritual food such as garden landscape after the material life is satisfied, which brings new challenges to the construction of urban garden landscape. As an advanced type of machine learning, deep learning applied to landscape image recognition can solve the problem of low quality and low efficiency of manual recognition. Based on this, this paper proposes a garden landscape image recognition algorithm based on SSD (Single Shot Multibox Detector), which realizes accurate extraction and recognition of image features by positioning the target, and can effectively improve the quality and efficiency of landscape image recognition. In order to test the feasibility of the algorithm proposed in this paper, experimental analysis was carried out in the CVPR 2023 landscape data set. The experimental results show that the algorithm has a high recognition accuracy for landscape images, and has excellent performance compared with traditional image recognition algorithms.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"26 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FCTNet: Fusion of 3D CNN and transformer dance action recognition network FCTNet：3D CNN 与变压器舞蹈动作识别网络的融合

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-235302

Tao Ning, Tingting Zhang, Guowei Huang

引用次数: 0

Advancing electric demand forecasting through the temporal fusion transformer model 通过时空融合变压器模型推进电力需求预测

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-236036

M. Karthikeyan, Ilhami Colak, S. Sagar Imambi, J. Joselin Jeya Sheela, Sruthi Nair, B. Umarani, Andril Alagusabai, K. Suriyakrishnaan, A. Rajaram

{"title":"Advancing electric demand forecasting through the temporal fusion transformer model","authors":"M. Karthikeyan, Ilhami Colak, S. Sagar Imambi, J. Joselin Jeya Sheela, Sruthi Nair, B. Umarani, Andril Alagusabai, K. Suriyakrishnaan, A. Rajaram","doi":"10.3233/jifs-236036","DOIUrl":"https://doi.org/10.3233/jifs-236036","url":null,"abstract":"This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"28 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering 基于多任务模糊聚类的新型多任务 TSK 模糊系统建模方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-20 DOI: 10.3233/jifs-232312

Ziyang Yao

引用次数: 0

Automatic detection of obsessive-compulsive disorder from EEG signals based on Hilbert-Huang transform and sparse coding classification 基于希尔伯特-黄变换和稀疏编码分类从脑电图信号中自动检测强迫症

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-19 DOI: 10.3233/jifs-237946

Yuntao Hong

{"title":"Automatic detection of obsessive-compulsive disorder from EEG signals based on Hilbert-Huang transform and sparse coding classification","authors":"Yuntao Hong","doi":"10.3233/jifs-237946","DOIUrl":"https://doi.org/10.3233/jifs-237946","url":null,"abstract":"Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"46 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing deep learning-based intrusion detection in cloud computing environment with chaotic tunicate swarm algorithm 利用混沌调谐群算法优化云计算环境中基于深度学习的入侵检测

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-19 DOI: 10.3233/jifs-237900

C. Jansi Sophia Mary, K. Mahalakshmi

{"title":"Optimizing deep learning-based intrusion detection in cloud computing environment with chaotic tunicate swarm algorithm","authors":"C. Jansi Sophia Mary, K. Mahalakshmi","doi":"10.3233/jifs-237900","DOIUrl":"https://doi.org/10.3233/jifs-237900","url":null,"abstract":"Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":"19 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献