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A scalable autotest platform for embedded system

A scalable autotest platform for embedded system

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International Conference on Apperceiving Computing and Intelligence Analysis, ICACIA

作者： Di Liu Qiao Su Yan Xie School of Computer Science& Engineering University of Electronic Science and Technology Sichuan China High Speed Aerodynamics Institute China Aerodynamics Research and Development Center China

The complex and diversity of embedded system make the developing and testing embedded system a hard work. Providing a high-availability high-scale universal testing platform can improve the development of embedded software. The autotest platform focuses on layered scalability and continuous integration. Loose coupling between the layers makes the system easy to extend at the local layer. Continuous improvements reduce the initial investment, optimize the platform implementation, and expand capacity and capabilities according to requirements. Users commit test requests front-end, and background centralized testing cluster executes this task and returns results to users.

关键词： Servers Testing Monitoring Hardware Embedded systems Schedules

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Compression Method for Convolution Kernel with KCP-FGTD

Compression Method for Convolution Kernel with KCP-FGTD

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International Conference on Information, Communication and Networks (ICICN)

作者： Rui Lv Jiangbin Zheng Siyu Tao Chenyu Zhang Chao Hu School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Information Science& Technology Northwest University Xi'an Shaanxi China School of Artificial Intelligence Xidian University Xi'an Shaanxi China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

Convolutional neural networks (CNNs) possess strong expressive capabilities, but with the increasing size and cost of modern CNNs, deploying large-scale CNNs in resource-limited environments remains a challenge, making neural network compression a hot topic in contemporary research. Among various compression methods, tensor decomposition shows numerous unique advantages, such as regular data structure, solid mathematical foundation, convenience in training from scratch, and ideal compression rates. However, most existing tensor decompositions do not perform well in compressing CNNs, struggling to balance time and space complexity, and failing to accelerate CNNs in reality. To address these issues. In this paper, we propose a fine-grained KCP tensor decomposition for CNN convolution kernel compression with considerable compression rate and acceleration capability. First, convolution kernels are compressed using 2nd-order KCP decomposition. The decomposed factor tensors are then converted into a four-layer lightweight convolution kernel structure. This structure conducts continuous contraction computations with the input, transforming the convolution computations into a highly efficient lightweight layered matrix multiplication structure. Secondly, we conduct a comprehensive comparison with other methods and analyze the superiority of our method. Comparisons of computational complexity with other typical tensor compression methods, along with simulation experiments on CIFAR-10 and ImageNet datasets, demonstrate that our method can achieve more efficient comprehensive results. These include acceptable accuracy, significantly reduced model size, and real-world acceleration of network inference times. Finally, we further analyze how different combinations of KT rank and CP rank impact the compression effectiveness and expressive capacity of CNNs.

关键词： Training Tensors Image coding Convolution Neural network compression Computational efficiency Convolutional neural networks Matrix decomposition Kernel Computational complexity

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The application research of improving neural network algorithm in the grain monitoring

The application research of improving neural network algorit...

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International Conference on Advanced computer Theory and Engineering, ICACTE

作者： Wu JianJun Bao Zhan Biao School of Computer Science& Technology Wuhan University of Technology Zhengzhou China College of Information Science and Engineering Henan University of Industry Zhengzhou China Henan University of Economics and Law Zhengzhou China

ISBN: (纸本)9781424465392;9781424465422

In allusion to the insufficiencies such as knowledge acquirement, reasoning ability and self-learning ability, the paper applies neural network into expert system, combines with the system of measurement and control for grain storage, and puts forward an improved BP algorithm. This algorithm does not need the prior hypothesis model and has a good compatibility to the complete and noisy information; it also can solve the non-linear problem well. This new algorithm has coordinated contradictions between learning efficiency and convergence rate and improved skilled speed and convergence rate. From the results of experiment, we can see that the new algorithm has some advantages, such as quickly, validity and practicability.

关键词： Monitoring Coordinate measuring machines Convergence

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Learning automaton based context oriented middleware architecture for precision agriculture

Learning automaton based context oriented middleware archite...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： P. Venkata Krishna S. Sivanesan Sudip Misra Mohammad S. Obaidat Department of Computer Science Sri Padmavathi Mahila Visvavidyalayam Tirupati India School of Information Technology Indian Institute of Technology Kharagpur India School of Computing Science and Engineering VIT University Vellore India Dept. of Computer Science& Software Engineering Monmouth University NJ USA

The continuous alteration of living landscape in earth due to the greatest impact of information technology has converted the whole earth into a small digital village. That means anyone could share information in any form with any other person residing anywhere in the earth. With the availability of information and communication technology like cloud computing, heterogeneous networking, crowd sensing, web services and data mining, anywhere and anytime information sharing is possible, but this will bring out lot of challenges like incompatibility in standards, data portability, data aggregation, data dissemination, differential context and communication overhead. The ICT has changed many aspects of human lifestyle, work places and living spaces. However, there is one sector namely agriculture which has been deprived of the real advantages of ICT. Hence, there exists a digital divide between farming industry and other industries. Farming industry consumes large amount of natural resources such as water, energy and fertilizers that could escalate issues such as global warming, soil degradation and depletion of ground water to the next level. To reduce the global warming effect, farming industry needs to be integrated with relevant technologies. This paper proposes ubiquitous context oriented middleware architecture for precision agriculture (LA-COMPa) to solve major issues such as waste of water, improper application of fertilizer, choice of wrong crops and season, poor yield and lack of marketing.

关键词： Context Learning automata Middleware Fertilizers Manuals Soil

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Improved Moth-Flame Optimization Based on Opposition-Based Learning for Feature Selection

Improved Moth-Flame Optimization Based on Opposition-Based L...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Mohamed Abd Elaziz Songfeng Lu Diego Oliva Mohammed El-Abd School of Computer Science& Technology Huazhong university of Science and Technology Wuhan China Departamento de Ciencias Computacionales Universidad de Guadalajara Guadalajara Jal Mexico Department of Engineering American University of Kuwait Safat Kuwait

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In this paper, an improvement for the Moth-flame Optimization (MFO) algorithm is proposed based on Opposition-Based Learning (OBL), that enhances the exploration of the search space through computing the opposition values of solutions generated by MFO. Moreover, such an approach increases the efficiency of MFO as multiple regions in the search space are investigated at the same time. The proposed algorithm (referred to as OBMFO) avoids the limitations of MFO (and other swarm intelligence algorithms) that result from the moving in the direction of the best solution, especially if this direction does not lead to the global optimum. Experiments are run using classical six benchmark functions to compare the performance of OBMFO against MFO. Moreover, OBMFO is used to solve the feature selection problem, using eight UCI datasets, in order to improve the classification performance through removing irrelevant and redundant features. The comparison results show that the OBMFO superiors to MFO for the tested benchmark functions. It also outperforms another three swarm intelligence algorithms in terms of the classification performance.

关键词： Feature extraction Sociology Statistics Optimization Benchmark testing Particle swarm optimization Convergence

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Advanced Technological Improvements in Making of Smart Production Using AI and ML 4

Advanced Technological Improvements in Making of Smart Produ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Praveenraj, D. David Winster Vijayaraj, T. Kumar, K. Sambath Sakthivel, N.K. Multani, Fahim Nagaveni, P. Christ University School of Business and Management TN Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering TN India Department of Electronics and Communication Engineering Chennai Tamil Nadu Avadi India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu T.M. Palayam India Ies College of Technology Department of Computer Science& Engineering M.P Bhopal India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering TN India

ISBN: (纸本)9798350360165

The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. © 2024 IEEE.

关键词： Smart manufacturing

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Facial Emotion Detection using CNN & VGG16 Model

Facial Emotion Detection using CNN & VGG16 Model

引用

International Conference for Convergence of technology (I2CT)

作者： Aryan Singh Negi Anamika Arora Sourav Bisht Swati Devliyal Bura Vijay Kumar Gurjot Kaur Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science& Artificial Intelligence SR University Warangal India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In fields like human-computer interaction, healthcare, and marketing, facial and emotional recognition utilizing machine learning (ML) has found many uses. The primary method used in this study is Convolutional Neural Networks (CNNs), more especially pre-trained models like ResNet and VGG16 that have been tweaked using labelled facial expression datasets. Enhancement techniques are used to enhance model generalization, and data preparation, ensemble learning, and transfer learning are used to address issues like posture variations and cultural expressions. In tests conducted on the benchmark datasets CK+ and FER-2013, competition-level performance is displayed in parameters such as accuracy, precision, recall, and F1-score. The application of this ML-based methodology in emotion-aware systems, human-computer interaction, and mental health monitoring is highlighted by these *** present work underscores the potential of machine learning (ML) in detecting facial expressions and emotions. Further research is necessary to improve the functionality of these systems in order to achieve greater social advantages.

关键词： Human computer interaction Emotion recognition Face recognition Transfer learning Mental health Medical services Data models

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Constrained optimization by the evolutionary algorithm with lower dimensional crossover and gradient-based mutation

Constrained optimization by the evolutionary algorithm with ...

引用

Congress on Evolutionary Computation

作者： Qing Zhang Sanyou Zeng Rui Wang Hui Shi Guang Chen Lixin Ding Lishan Kang Huanggang Normal University Hubei China School of Computer Science Technology Research Center for Spacece Science & Technology and The State Key Laboratory of Geological Processes and Mineral Resources China University of Geoscience Wuhan China School of Computer Science Technology Research Center for Spacece Science& Technology and The State Key Laboratory of Geological Processes and Mineral Resources China University of Geoscience Wuhan China StateKey Laboratory of Software Engineering Wuhan University of China Wuhan Hubei China

This paper proposes a new evolutionary algorithm with lower dimensional crossover and gradient-based mutation for real-valued optimization problems with constraints. The crossover operator of the new algorithm searches a lower dimensional neighbor of the parent points where the neighbor center is the barycenter of the parents, and therefore the new algorithm converges fast. The gradient-based mutation is used to converge fast for the problems with equality constraints and active inequality constraints. And the new algorithm is simple and easy to be implemented. We have used 24 constrained benchmark problems to test the new algorithm. The experimental results show it works better than or competitive to a known effective algorithm.

关键词： Evolutionary computation Optimization Equations Evolution (biology) Algorithm design and analysis Geology Constraint optimization

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors 2

Investigation of Environmental Monitoring Based on Three IoT...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Arthy, M. Priya, S Geetha Kathiresan, S. Jeneetha Jebanazer, J. Uthayakumar, G.S. Srm Institute of Science and Technology Department of Networking and Communication Engineering Tamil Nadu Kattankulathur Chennai India Srm Institute of Science and Technology School of Computing Department of Data Science& Business Systems Tamil Nadu Kattankulathur Chennai India R. M. D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User Datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability. © 2023 IEEE.

关键词： Bluetooth

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Evaluation of Contingency-Based Learning Strategies for Precision Medicine

Evaluation of Contingency-Based Learning Strategies for Prec...

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International Conference on Computing and Networking technology (ICCNT)

作者： Harshita Kaushik Naveen Kumar Rajendran Ashwini Kumar Department of Computer Science& Application Vivekananda Global University Jaipur India Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Contingency-based learning strategies primarily based on synthetic intelligence (AI) are being hired within the area of precision remedy to enable greater powerful selection-making when treating patients. The overall performance of such learning strategies should be evaluated to make sure they are presenting accurate and dependable outcomes. This paper aims to assess contingency-based learning strategies for precision medication to help clinicians and researchers appropriately assess the efficacy of these learning techniques and decide the first-rate method for given software., a scientific evaluation of the current literature was undertaken to identify existing assessment strategies and analyze their strengths and weaknesses. The assessment criteria used in this analysis are accuracy, interpretability, complexity, scalability, and transferability, which had been selected as they affect the performance of AI-primarily based approaches in real-international settings. Furthermore, a discussion of current equipment and frameworks that could be used to assess AI-based techniques in the area of precision remedy was performed. From this assessment, it has been discovered that most of the present assessment techniques lack the necessary accuracy and transferability essential to appropriately check the efficacy of AI-based strategies for precision medicinal drugs. But, sure, evaluation metrics, together with explain ability, may be employed to degree the overall performance of AI-primarily based techniques in order to decide the nice approach for a given application.

关键词： Drugs Measurement Privacy Accuracy Scalability Precision medicine Contingency management Software Safety Medical diagnostic imaging

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