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Image as a Way of Processing Multidimensional Production data for Product Quality Prediction Using Deep learning 3rd

Image as a Way of Processing Multidimensional Production Dat...

引用

3rd international conference on Innovation in engineering, ICIE 2024

作者： Paśko, Lukasz Setlak, Galina Faculty of Mechanical Engineering and Aeronautics Rzeszów University of Technology Powstańców Warszawy 8 Rzeszów35-959 Poland

ISBN: (纸本)9783031615740

The article concerns the processing of multidimensional data sets that are obtained from production processes and are used as training data for predictive models. Preparing such training data often requires performing complex feature engineering operations to remove highly correlated features or select only the most important features. The article proposes an approach that minimizes the number of feature engineering operations by using encoding feature values in the form of images. This approach was tested on data about the parameters of the manufacturing process in a glassworks. This complex data set, which contains many highly correlated features, was transformed into images. Two image-coding methods were tested. The sets of images were used to train convolutional neural networks (CNNs), which perform the task of predicting the level of defective products. The research conclusions show that the method of encoding features influences the selection of the optimal CNNs architecture. The CNN classifiers were compared with benchmark models (BMs) trained on original data, not transformed into images. The results obtained showed greater effectiveness of CNNs compared to BMs. The article may be an inspiration for further research on simplifying and automating the feature engineering process, especially for multidimensional data sets from manufacturing processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Latent ODEs-based data compression method for power system 3

Latent ODEs-based data compression method for power system

引用

2023 3rd international conference on Electrical engineering and Computer Technology, ICEECT 2023

作者： Xue, Xiangxi Su, Ya School of Electrical Engineering Shandong University Jinan Shandong250061 China

In order to effectively compress the power system data and reduce the space occupied by data storage and transmission, this paper proposes a data compression method based on Latent Segmented ODE (LatSegODE) for the large-scale steady-state data generated during the working process of the power system. By training the ODE model using a large amount of steady-state data, a latent ODE model is generated to characterize the steady-state data of the power system, thereby significantly reducing the storage space occupied by the data. During the experimental stage, compression testing is carried out on the static data of the power system of a power grid company. According to the quantitative evaluation results of the two indices of normalized mean square error and data compression ratio, the proposed method has obvious compression advantages, and the compressed signals have a high degree of fitting to the actual sampled signal waveforms. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Design and Research of an intelligent Dimming System for Electronic Table Lamp Based on 51 Microcontroller 3

Design and Research of an Intelligent Dimming System for Ele...

引用

3rd international conference on intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Xue, Tongmei College of Electronic Science and Engineering Jilin University Jilin Province Changchun130000 China

ISBN: (纸本)9781510683105

This design scheme aims to create an intelligent electronic dimming table lamp with manual and automatic control functions, which can achieve flexible brightness adjustment, automatically turn off the lamp when walking, and provide a more intelligent experience through a real-time display of power and distance. In addition, the design also supports Bluetooth control on mobile phones, allowing users to achieve the same functions as buttons through their phones, which can meet the growing demand for smart furniture. This design scheme not only achieves the basic requirements of turning on, adjusting brightness, and automatically dimming the electronic dimming table lamp, but also provides additional functions such as working time setting and recording, automatic reduction of brightness, real-time display of voltage, current, and power. To achieve the above functions, the design scheme adopts multiple key components, including a 4x4 matrix keyboard for operation control, a 128×64 0.96-inch OLED display screen for displaying various parameters, and a GY-39 light intensity sensor module for detecting ambient light brightness. Using the STC15F2K60S2 minimum system board as the main controller, the output PWM signal controls the bidirectional thyristor voltage regulation module to adjust the voltage, achieving precise control of the brightness of the incandescent lamp. At the same time, the IM1253B AC/DC AC coulometer is used to measure and display the working voltage, current, and power data of the table lamp. The software control adopts a method combining interrupt and polling. It applies ultrasonic modules to remind users to turn off the lights in a timely manner by measuring the distance between people and lamps, thereby achieving energy conservation and environmental protection. © 2024 SPIE.

关键词： Thyristors

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Transformer-Based Adversarial Imitation learning: Enhancing Long-Term Decision Making with Time Series 3rd

Transformer-Based Adversarial Imitation Learning: Enhancing ...

引用

3rd international conference on Machine learning, Cloud Computing and intelligent Mining, MLCCIM 2024

作者： Zhang, Yawen Shanghai University Shanghai200444 China

ISBN: (纸本)9789819624676

Imitation learning offers a high-performance and versatile solution for addressing various complex decision-making problems in the real world. Generative Adversarial Imitation learning (GAIL) is a highly potential algorithmic framework in the field of imitation learning, where high-performance policies can be obtained by computing imitation rewards based on a given series of expert datasets. However, existing algorithms based on the GAIL framework predominantly focus on the current state and seldom consider the influence of time series on the acquisition of imitation rewards. Using time series is an effective and efficient method to solve the long-term dependency problem in long-horizon robotic tasks. The algorithm we propose combines the GAIL algorithm with time series, utilizing a Transformer encoder to obtain representations of state sequences. It generates imitation rewards using an attention-based representation. To address the instability in the imitation rewards caused by the large model size, we employed a range mapping method to enhance stability. The GAIL algorithm, with our proposed imitation reward, achieves commendable results across DeepMind Control Suite. We also experimented with different lengths of time series and sequence models, demonstrating the effectiveness of integrating time series with the GAIL framework using transformers. Our method attained 92.53% of the expert reward in the walker_run environment, significantly outperforming the GAIL framework using Temporal Convolutional Network (TCN), which only achieved 10.28% of the expert reward. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Machine learning Pose Detection Kit Implementation in Taspen Android Application 3

Machine Learning Pose Detection Kit Implementation in Taspen...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Habbe, Abdul Hamid Farida Adi Prawira, Ida Muda, Iskandar Hasibuan, Rizki Mayandi Dhumale, N.R. Universitas Hasanuddin Accounting Department Makassar Indonesia Accounting Department Universitas Pendidikan Indonesia Bandung Indonesia Accounting Department Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Medan Indonesia Sinhgad College of Engineering Pune India

ISBN: (纸本)9781665462167

The purpose of this paper is to determine that a machine learning kit for live detection is able to minimize the time consumed or time efficiency from offline pension live detection to activate the monthly pension recipient bank account compared to online methods that are available now. Time efficiency measured by time needed to complete the task to check the lives of pension recipients offline and online. The research case application for this paper is live detection in Taspen authentication android application that is available on google play store for online, and old offline process that obliged the pension recipient to come to PT. Taspen office directly. The methodology to measure time efficiency is very efficient. In broad outline, the results of time efficiency are very satisfied compared to online live check. The result is making the conclusion that online live detection is more efficient than the old offline method to detect the lives of pension recipients. Note that this paper is a conceptual paper without any data to collect and also analyze, but just to determine that machine learning has a significant role and is productive and also useful in financial technology. © 2023 IEEE.

关键词： Machine learning

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Study on the classification method of gastrointestinal endoscope image based on CBAM and AlexNet 3

Study on the classification method of gastrointestinal endos...

引用

3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Tong, Huifeng Zhou, Hua College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510667563

Gastrointestinal diseases are one of the important causes of gastrointestinal tumors, and also one of the serious public health problems that human beings are currently facing. Gastrointestinal inflammation and polyp lesions are the main ways to cause gastrointestinal cancer. Therefore, timely detection of gastrointestinal polyps and inflammation and other lesions through gastrointestinal endoscopy can effectively prevent corresponding cancer. In recent years, deep learning technology has played a huge role in computer-aided gastrointestinal endoscopic diagnosis, especially the convolutional neural network can recognize gastrointestinal lesions. This paper uses the public gastrointestinal endoscopy data set Kvasir (including 7 categories, 1000 images for each category), proposed and designed CBAM-AlexNet deep learning model, in which CBAM is the attention mechanism part, including channel attention and spatial attention, AlexNet is a deep learning network. The original features are input into the channel attention mechanism in CBAM to extract channel features, and then the output channel features are input into the spatial attention mechanism to extract spatial features, and finally the output spatial features are input into the AlexNet network for feature extraction and gastrointestinal disease data. The average accuracy rate, average accuracy rate, average recall rate and average F1Score of all categories of CBAM-AlexNet model were 91.36%, 91.34%, 91.39% and 0.913, respectively, and achieved good classification effect in seven categories of gastrointestinal endoscopy data, showing the superiority of the model. CBAM-AlexNet model has high specificity and sensitivity for the recognition of gastrointestinal polyps, which can assist doctors in the clinical diagnosis of gastrointestinal polyps quickly and reduce the probability of misdiagnosis, Some recognition errors caused by quality issues such as overexposure or underexposure of images will be improved in future wo

关键词： Endoscopy

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Developing automated Optical Character Recognition System Using Machine learning Algorithm to Solve Payment Verification Issues 3

Developing Automated Optical Character Recognition System Us...

引用

3rd international conference on Cybernetics and intelligent System (ICORIS)

作者： Siek, Michael Soeharto, Rafi Bina Nusantara Univ Business Informat Syst Program Informat Syst Dept Fac Comp & Media Jakarta 11480 Indonesia

ISBN: (纸本)9781665425803

One primary aspect in customer services is to provide immediate solution towards payment verification issues, such as a time delay of payment confirmation by the payment service provider or supplier. This paper presents a development of an accurate optical character recognition (OCR) system using convolutional neural network with deep learning algorithm, which can skip some steps in the workflow of manual payment approval to fasten the process of payment verification and confirmation. By using some machine learning frameworks of pyTorch utilizing Tensors and CUDA-GPU parallel computing, the machine learning based OCR system was developed and tested with the actual data. The real data sets used here cover the non-uniformity of the receipt bill's papers with various conditions (crumple, water drops, and folds) with some nature of the customer's overall camera noise, angle, and lighting. Several experiments associated with data preparation, deep learning parameter settings, and model performance comparison, were properly conducted to obtain a high quality of OCR system to detect trace number, approval codes, and nominals on the widely-used payment receipts. The resulting OCR system performed very satisfactory with 100% accuracy on testing data set. This promising results permit for the integration between this accurate and automated OCR system and chat environment with chatbot technology in order to provide better user experience and immediate and reliable solution toward payment verification issues.

关键词： Convolutional neural network deep learning algorithm electronic payment technology using text recognition

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A Novel Cryptographic Algorithm for Securing data in Cloud Computing 3rd

A Novel Cryptographic Algorithm for Securing Data in Cloud C...

引用

3rd international conference on Artificial-Business Analytics, Quantum and Machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Dagur, Arvind Singh, Mani Shakya, Abhay Pratap School of Computing Sciences and Engineering Galgotia’s University Greater Noida India

ISBN: (纸本)9789819725076

As increase in the technology like Internet of things and Cloud computing the demand of data security is also increasing. Because the people’s data is not safe on the cloud and the internet. So here the encryption algorithms came into existence. Encryption means convert the plain text into cipher-text, It is necessary that the cipher-text must be secured and efficient so that it can also be transmit over the cloud and the internet without any leak or theft of data. It is important to provide a strong level of protection against unauthorized access. Some popular encryption techniques are available to us are AES (Advance Encryption Standard), DES (data Encryption Standard), TRIPLE DES, BLOWFISH, RC5, SHA1 etc. In this research various encryption techniques are discussed and their advantage and disadvantages. In this paper, a new encryption technique is implemented on the data to make it more secure and reliable than the available encryption techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Encryption algorithms

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Advanced Camouflage Robot for Military Spying Applications using Sensor Fusion and Machine learning 3

Advanced Camouflage Robot for Military Spying Applications u...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kulkarni, Neeraj P Khan, Subim N. Rathod, Rahul Ramkishan Choure, Ajay Raosaheb Chinchole, Mahesh Gokul Jspm Rajarshi Shahu College of Engineering Tathawade Pune India Soet D Y Patil University Ambi Pune India Bhivarabai Sawant Institute Of Technology And Research E&tc Department Wagholi Pune India Nutan Maharashtra Institute of Engineering and Technology E&tc Department Pune India

ISBN: (纸本)9798350354348

The current defense application scenario. the ability to blend into the environment is crucial for reducing detection and enhancing operational efficiency. This paper presents the development of an advanced camouflage robot to dynamically adapt its color to match its surroundings. The proposed system utilizes a combination of machine learning, computer vision, and sensor fusion to achieve camouflaging using the RGB LED panels mounted on the robot to adapt to the color of the surroundings. The robot is equipped with a video camera system that captures real-time video feed of the environment. The captured video is then processed by a trained machine learning model, which analyzes the color distribution in the surroundings. Simultaneously, the robot uses RGB color sensors to accurately detect the colors present in its immediate vicinity. The data from both the camera and the sensors are integrated to determine the predominant color around the robot. The hybrid camouflaging approach involves feeding the combined data into the machine learning model, which then calculates the color of maximum distribution. The robot then activates its RGB panels, adjusting their color to blend with the identified predominant color in the environment thereby making it difficult to locate. This process allows the robot to effectively camouflage itself, minimizing its visual footprint. KNN and sensor fusion techniques are used to achieve this task. © 2024 IEEE.

关键词： Nearest neighbor search

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Multi-Modal Deep learning Architecture for Accurate Driver Behavior Recognition in automated Driving Systems 18

Multi-Modal Deep Learning Architecture for Accurate Driver B...

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18th international conference on intelligent Systems and Knowledge engineering, ISKE 2023

作者： Alhaek, Fares Liang, Weichao Rajeh, Taha M. Javed, Muhammad Hafeez Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350318401

automated driving systems and advanced driver assistance systems require accurate and reliable driver behavior recognition models to ensure safe and efficient operation. The complexity of background contents in real-world driving scenarios presents a significant challenge. Moreover, the distinction between different driving behaviors can be extremely subtle. Minor variations in body movements, gestures, or vehicle control, which are challenging to discern accurately, may indicate distinct behaviors. To address the above issues, this paper proposes a novel multi-model deep learning architecture (MMDLA) for driver behavior recognition by integrating multiple modalities, including RGB frames, depth information, and skeleton data, to provide a more comprehensive representation of driver behavior. The proposed model consists of two stages. In the first stage, we use local and global feature extraction to extract features from RGB and Depth frames. Additionally, we construct a graph from 3D skeleton data and feed it to a graph convolutional network to extract its features. In the second stage, we perform ensemble learning and fuse the diversity of multiple features together. Experiments on the Drive&Act dataset shows that our model achieves superior performance, outperforming existing models on both hierarchical and fine-grained activity recognition tasks. © 2023 IEEE.

关键词： Advanced driver assistance systems

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