Efficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and...
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Efficient heat exchanger design is paramount in optimizing chemical processing operations, where energy consumption and cost considerations are crucial. Traditional design approaches rely on empirical correlations and iterative simulations, often resulting in suboptimal solutions due to the complex and nonlinear nature of heat transfer phenomena. In this study, the research proposes a novel approach to enhance heat exchanger design using an autoencoder model for predicting both efficiency and cost. The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. By learning the underlying patterns and relationships within the data, the autoencoder can effectively capture the nonlinear mappings between design parameters and performance metrics. Through extensive validation and testing, the proposed autoencoder model demonstrates superior accuracy in predicting heat exchanger efficiency and cost compared to conventional methods. Furthermore, the model enables rapid exploration of design alternatives and sensitivity analysis, facilitating informed decision-making in the design phase. By leveraging machine learning techniques, this approach offers a promising avenue for advancing heat exchanger design towards higher efficiency and lower cost in chemical processing applications. The framework demonstrates considerable promise in bolstering efficiency and enhancing economic viability, boasting a correlation coefficient of 0.98171 and a normalized root mean square error (NRMSE) of 0.001523.
In order to monitor all telemetry data, thresholds are adopted to judge the status of satellite. This method is terrible when some abnormal happened, if the data was not more than pre-set threshold. when the data exce...
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ISBN:
(纸本)9781450388085
In order to monitor all telemetry data, thresholds are adopted to judge the status of satellite. This method is terrible when some abnormal happened, if the data was not more than pre-set threshold. when the data exceeding the threshold after a period of time, there were a big fault for satellite. This fault would make a huge economic loss especially for the communicate satellite. These are two classes telemetry of satellite about this scenario, one class is continuously changing digital telemetry, the other class is temperature. A method was proposed for solving these problems. An autoencoder model was applied to monitor the telemetry data according to the devices or equipment board. Each device or equipment board has own model, and telemetry data is inputted to the model for compressing a single parameter as one-dimension feature. The operators just only monitor the one-dimension feature, that is simple and fast. If an abnormal appear, the parameter of device or equipment board would be changed to warn the operators, who would check the actual telemetry data of device or equipment board, and the abnormal would be checked out immediately and earlier than the traditional method. For detecting the two kinds of typical abnormal which could not detect by traditional method, two models were built and data was prepared. The results show that auto-decoder model can detect the abnormal accurately and be useful for the operator. A software was built, and some models were trained for a satellite.
Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data's underlying distr...
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Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data's underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques' applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor's variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures;LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the com
This paper uses the U-Net model to accurately analyse remote sensing data and applies it to ecological landscape restoration planning and pollution prevention to improve environmental monitoring efficiency, optimise r...
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This paper uses the U-Net model to accurately analyse remote sensing data and applies it to ecological landscape restoration planning and pollution prevention to improve environmental monitoring efficiency, optimise restoration strategies, and help achieve sustainable management of ecosystems. First, a large amount of remote sensing image data is obtained through Landsat satellite images and preprocessed. Then, the U-Net model is used to analyse the remote sensing data, identify different types of land objects, and monitor pollution sources in the environment. autoencoder is used to detect abnormal areas in the land object classification results, and finally a support vector machine is used to classify the pollution sources. The results show that the average accuracy of the U-Net model in classifying landscape features reaches 95.4%, and the use of autoencoder can accurately detect abnormal areas;the combination of U-Net and remote sensing data can achieve accurate classification of features.
作者：
Malini, P.Kavitha, K. R.Anna Univ
Vivekanandha Coll Technol Women Dept Elect & Commun Engn Tiruchencode 637205 Tamil Nadu India Sona Coll Technol
Dept Elect & Commun Engn Salem 636005 Tamil Nadu India
In recent years, the development of Internet of Things (IoT) applications has increased, resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of service (QoS). In advanced communicati...
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In recent years, the development of Internet of Things (IoT) applications has increased, resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of service (QoS). In advanced communications, managing network resources for allocating IoT services and identifying the exact IoT devices connected to a network is a major concern. The existing studies have introduced various methods for classifying IoT devices in a network. However, the previous studies faced challenges like limited attributes, low efficiency, inappropriate features, and computational complexities. Also, the existing studies failed to concentrate on IoT/Non-IoT classification along with attack detection. Detecting attacks on IoT devices is critical for making network services more effective. Thus, the proposed study introduces an efficient IoT device classification and attack detection mechanism using software defined networking (SDN)-enabled fiber-wireless access networks internet of things (FiWi IoT) architecture. Initially, an effective resource allocation process is performed to mitigate the delay constraint issues by introducing a hybrid parallel neural network-based dynamic bandwidth allocation (DBA) method. Then, the input traffic information is gathered from the resource-efficient SDN-enabled FiWi IoT network, and the input data is pre-processed to eliminate unwanted noises using min-max normalization and standardization. Next, the essential attributes are extracted to attain enhanced classification performance. To reduce the feature dimensionality problem and thereby solve complexity issues, the most optimal features are selected by a new chaotic seagull optimization (CSO) approach. After that, IoT/non-IoT classification is performed using a transformer-driven deep intelligent model. Finally, the attacks are detected and classified by introducing a novel slice attention-based deep capsule autoencoder (SA_DCAE) model. For experimentation, the Python 3.7.0 tool is use
Electrocardiogram (ECG) signals are central to cardiac health assessment but interpreting them accurately requires expertise. Traditional methods often lack interpretability, posing limitations in ECG signal analysis....
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Self-supervised video hashing aims at generating hash codes and performing fast video content retrieval by leveraging the visual content information inherent in the videos themselves. Most existing methods often overl...
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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. D...
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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.
To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach a...
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To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event -based model to create and visualize rule -based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event -based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest.
Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosy...
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Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems.
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