NIKHIL R. MENE¹, SARITA KALLA^1,*, Z.V.P. MURTHY^1,*

Journal of Polymer Materials, Vol.39, No.3-4, pp. 307-323, 2022, DOI:10.32381/JPM.2022.39.3-4.9

Abstract Membrane distillation-crystallization (MDC) is presented as a novel technique in the treatment of waste concentrated water which produces valuable crystals along with pure water. In the present study, multi-walled carbon nanotubes (MWCNT)/polyvinylidene fluoride (PVDF) flat sheet membranes were prepared via the wet phase inversion method and applied in MDC for the treatment of pharmaceutical waste. The pure and modified membrane surface properties are characterized with the help of SEM, FTIR, and contact angle measurement. The present work reported the effect of MWCNT content and feed temperature on the MDCperformance and measured pure water flux and pharmaceutical compounds recovery. The observed… More >

Xue Li, Hongying Zhang^*, Zixun Ye, Xiaoru Huang

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2847-2864, 2024, DOI:10.32604/cmc.2024.047093

Abstract Transformer-based stereo image super-resolution reconstruction (Stereo SR) methods have significantly improved image quality. However, existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information. To address these challenges, this paper introduces a novel epipolar line window attention stereo image super-resolution network (EWASSR). For detail feature restoration, we design a feature extractor based on Transformer and convolutional neural network (CNN), which consists of (shifted) window-based self-attention ((S)W-MSA) and feature distillation and enhancement blocks (FDEB). This combination effectively solves the problem of global… More >

Soonshin Seo^1,2, Ji-Hwan Kim^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2833-2856, 2023, DOI:10.32604/cmc.2023.042816

Abstract Automatic speech recognition (ASR) systems have emerged as indispensable tools across a wide spectrum of applications, ranging from transcription services to voice-activated assistants. To enhance the performance of these systems, it is important to deploy efficient models capable of adapting to diverse deployment conditions. In recent years, on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios. However, these methods often confront substantial trade-offs, particularly in terms of unstable accuracy when reducing the model size. To address challenges, this study introduces two crucial empirical findings. Firstly, it proposes the incorporation of… More >

Hong Zhu^*, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3363-3377, 2023, DOI:10.32604/cmc.2023.040731

Abstract Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates in the blockchain. The algorithm… More >

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >

Daniel Yabrudy Mercado^a,*, Juan Fajardo Cuadro^a, Bienvenido Sarria López^b, Camilo Cardona Agudelo^b

Frontiers in Heat and Mass Transfer, Vol.15, pp. 1-12, 2020, DOI:10.5098/hmt.15.25

Abstract This paper presents the efficiency-centered maintenance method to plan the maintenance intervention of the heat exchangers of a preheat train, taking into account the economic-energy improvement and maintenance cost. An appropriate cleaning schedule is needed to preserve the key performance parameters (KPPs) throughout the operation, if possible, nearest to the design values. The results of this work show that it is possible to schedule maintenance activities based on KPPs such as effectiveness and determine the time of execution and the type of maintenance that is most cost-efficient, without affecting and complementing the criteria for maintenance schedules based on reliability/risk. More >

Ling Wang, Jingchi Jiang^*, Jingwen Song, Jie Liu

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402

Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >

Le Quang Thao^1,2,*, Duong Duc Cuong², Vu Manh Hung³, Le Thanh Vinh³, Doan Trong Nghia⁴, Dinh Ha Hai³, Nguyen Nhan Nhi³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3517-3530, 2023, DOI:10.32604/iasc.2023.031026

Abstract The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student’s ophthalmic health because of their young age and a relative lack… More >

Yang Wu¹, Lei Guo¹, Xiaodong Dai¹, Bin Zhang¹, Dong-Won Park², Ming Ma^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5911-5924, 2022, DOI:10.32604/cmc.2022.031664

Abstract Action recognition has become a current research hotspot in computer vision. Compared to other deep learning methods, Two-stream convolutional network structure achieves better performance in action recognition, which divides the network into spatial and temporal streams, using video frame images as well as dense optical streams in the network, respectively, to obtain the category labels. However, the two-stream network has some drawbacks, i.e., using dense optical flow as the input of the temporal stream, which is computationally expensive and extremely time-consuming for the current extraction algorithm and cannot meet the requirements of real-time tasks. In this paper, instead of the… More >

Samar Elbedwehy^1,*, T. Medhat², Taher Hamza³, Mohammed F. Alrahmawy³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1483-1500, 2022, DOI:10.32604/cmc.2022.029313

Abstract Image captioning is an emerging field in machine learning. It refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an image. Image captioning requires a complex machine learning process as it involves two sub models: a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful captions. Attention-based vision transformers models have a great impact in vision field recently. In this paper, we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different… More >