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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

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How Mature is Requirements engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions

arXiv

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arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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A Novel and Efficient Sequential Learning-Based Malware Classification Model

A Novel and Efficient Sequential Learning-Based Malware Clas...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Zine El Abidine Bensalem Ilyas Benkhaddra Mohamed Ali Setitra Mingyu Fan School of Computer Science and Engineering (Cyberspace Security) University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665493901

The proliferation and increasing sophistication of malicious programs and other security risks have been identified as the most significant problem of the modern cybersecurity age. Malicious software, named malware, carries out harmful operations, causing abnormal functioning, data leakage, and crippling financial effects. Therefore, the door is widely open for one of the most crucial realms, so-called deep learning-based malware classification techniques, to thwart malware attacks. Malware's structure evolves significantly over time, making detecting them challenging. Malware invokes API call sequences while executing, so call sequences from APIs make excellent candidates for features in malware classification. Different malware samples can contain API call sequences with lengths ranging from one to millions, which can raise computation costs and complicate processing times; selecting an efficient set of features is yet another challenge. Recurrent neural networks (RNNs) are one of the most adaptable techniques for handling time-series data and are used to classify malware based on API calls. To efficiently categorize malware, in this study, a novel and efficient Long Short Term Memory model has been designed and divided into eight categories: Adware, Backdoor, Downloader, Dropper, spyware, Trojan, Virus, and Worm. The achieved results in terms of recall, precision, and F1 values are notable across several classes, whereas the adware class has the most significant result with a recall value of 80%. The proposed LSTM-based method outperformed conventional methods with a weighted F1 score of 48%.

关键词： computers Recurrent neural networks Computational modeling Malware Trojan horses Spyware Long short term memory

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Overview Paper A Comprehensive Survey of Digital Image Steganography and Steganalysis

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APSIPA Transactions on Signal and Information Processing 2024年第1期13卷

作者： Luo, Weiqi Wei, Kangkang Li, Qiushi Ye, Miaoxin Tan, Shunquan Tang, Weixuan Huang, Jiwu School of Computer Science and Engineering Sun Yat-sen University China School of Software Nanchang University China Shenzhen Key Laboratory of Media Security Shenzhen University China Institute of Artificial Intelligence Guangzhou University China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University China

In the realm of digital communications, steganography and steganalysis have become a solution for securely exchanging covert information. This survey initiates with an exploration of the widely used passive-warden scenario model, analyzing its significance, key performance indicators, relevant databases, and clarifying some commonly misunderstood fundamental concepts associated with this model. Subsequently, the paper comprehensively examines the evolution and current state of digital image steganography and steganalysis, highlighting the transition from traditional handcrafted based methods to sophisticated deep learning based techniques developed over the past two decades. It offers thorough descriptions and evaluations of typical methods in both steganography and steganalysis, with a particular emphasis on deep learning-based techniques that have emerged in recent years. Furthermore, the survey identifies significant challenges currently faced in translating theoretical research into practical applications. By integrating these insights, the survey not only charts the historical development and technological advancements in steganography and steganalysis but also establishes a proactive agenda for future research aimed at enhancing security in covert communications. © 2024 W. Luo, K. Wei, Q. Li, M. Ye, S. Tan, W. Tang and J. Huang.

关键词： Steganalysis steganography

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Efficient Joint Parameter Estimation and Soft Noncoherent Detection Scheme for LoRa-based IoT System

Efficient Joint Parameter Estimation and Soft Noncoherent De...

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Asia-Pacific Conference on Communications

作者： Zhongyang Yu Zhengying Wen Jixun Gao Yuqian Yan Chunhua Zhu School of Computer Science Henan University of Engineering Zhengzhou China School of Software Henan University of Engineering Zhengzhou China Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou China

Considering limited pilot overhead and large delay and Doppler shift in LoRa (Long Range)-based IoT (Internet of Things) systems, this paper presents an efficient joint parameter estimation and soft nonconherent detection scheme. During the parameter estimation, a joint delay and Doppler shift estimation method is proposed, which is called the JDDSE method. Different from the existing estimation methods, the proposed method utilizes the time-domain characteristic of the LoRa signal and only needs to estimate a joint offset of the delay and Doppler shift instead of their respective offsets. Then, a soft noncoherent detection is developed from the existing work in order to match the JDDSE method. Simulation results indicate the validity of the proposed method under the uplink IoT transmission.

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Multi-Angle Bidirectional Interaction Model for Entity Linking

SSRN

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SSRN 2022年

作者： Hui, Bei Zhang, Lizong Nian, Yuhui Yan, Ke Chen, Jia School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Entity linking is the task of linking the mention in the text to the corresponding entity in the knowledge graph. It is the way to achieve entity disambiguation which can be used in information retrieval, question and answering, content analysis, knowledge base population. Entity link models learn local compatibility through contents and learn global interdependence through correlation knowledge graph for disambiguation. However, the local compatibility component is usually implemented with a traditional unidirectional attention mechanism, ignoring the interaction between mention and candidate entity in multiple angles. Therefore, we propose the BI-INTEL model, which comprehensively considers the bidirectional interaction between the input document and the knowledge graph from multiple perspectives. In the local compatibility component, we both consider the relevance between mention and candidate entities and the relevance of mention context and description of candidate entity, obtaining the relevance of local information from two perspectives. In the global interdependence component, our stacked random walk layers learn the global interdependence of the candidate entity to enhance the accuracy of entity links. Experiments show that our proposed method has significant improvement compared with current state-of-the-art methods in entity link benchmarks such as AQUAINT, ACE2004, WNEDCWEB (CWEB), etc . © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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Design of Vehicle-cloud Collaborative Framework for 5G Cross-border Multi-PLAN Scenarios

Design of Vehicle-cloud Collaborative Framework for 5G Cross...

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International Conference on Network and Information Systems for computers (ICNISC)

作者： Guotao Du Jinlong Chu Jing Ma Shiduo Ning School of Mechanical Engineering Dalian University of Technology DUT Dalian China Department of Computer Science & Software Engineering School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665453523

The 5G-MOBIX project applies 5G network communication technology and defines five user stories and specific user scenarios in order to solve the cross-border problem of 5G driverless vehicles. Combined with the remote control user scenario in the remote driving user case, this paper explains the interconnect delay problem between PLMNS in this scenario. From the perspective of infrastructure, that is, the cooperation between vehicle infrastructure and cloud infrastructure, the CCAM architecture of remote control user story simulation test scenario is designed. The proposed solution of direct interconnection is verified and tested, and the cross-border test is simulated at the test site in China. The final test results show that the solution is feasible.

关键词： computers 5G mobile communication Collaboration computer architecture Communications technology Delays Remote control

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Effect of Enhanced Neurofeedback Stimulus Content on Frontal Alpha Asymmetry 5

Effect of Enhanced Neurofeedback Stimulus Content on Frontal...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Ali, Syed Saad Azhar Hafeez, Yasir Ariffin, Tajol Ashraf Bin Tajol Adil, Syed Hasan Amin, Hafeez Ullah Boon, Tang Tong Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Teknologi Petronas Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Neurofeedback training has various therapeutic effects to mitigate the mental illness such as stress, anxiety, and depression. Neurofeedback is a non-invasive self-training of brain with the help of auditory or visual input stimulus. The enhancement to neurofeedback stimulus content can help to improve the efficacy of neurofeedback training. Therefore, in this study, the game stimulus content is enhanced to find the effect of enhanced parameters towards the efficacy of neurofeedback. The enhanced parameters include the changing of background, colour contrast, and background music. The neurofeedback training sessions are performed to mitigate the stress among university students. The electroencephalogram (EEG) data is recorded and feedback from the participants are also taken. The graphical and statistical analysis of the results of the neurofeedback training using alpha asymmetry show the effectiveness of neurofeedback towards mitigation of stress. © 2022 IEEE.

关键词： Diseases

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A multi-source fusion method to identify biomarkers for breast cancer prognosis based on dual-layer heterogeneous network

A multi-source fusion method to identify biomarkers for brea...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyi Li Zhelin Zhao Ju Xiang Jialu Hu Xuequn Shang School of Computer Science Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The prognosis of breast cancer is challenging, which is an urgent problem to be solved. The prognostic biomarkers for breast cancer can help us predict the clinical outcomes of patients, and network-based methods are widely introduced to find prognostic biomarkers. According to the difference of input biological data, existing network-based biomarker prediction methods are mainly classified into two types: integrating single-source network or multi-source networks. However, the interactome of single-source network remains incomplete, and biological networks are noisy, which will hamper the network-based identification accuracy of biomarkers. In this study, we introduce a multi-source fusion method, DualMarker, which integrates multiple biological information sources and constructs a dual-layer heterogeneous network by fast network embedding. Next, we introduce a network enhancement method to denoise the constructed dual-layer heterogeneous network, and we implement network propagation algorithm on the constructed dual-layer heterogeneous network to rank the features. After comparing with competitive methods, we find that DualMarker substantially outperforms these methods. In addition, we verify that the biomarkers identified by DualMarker are closely related to the prognosis of breast cancer patients.

关键词： Prediction methods Biomarkers Heterogeneous networks Breast cancer Biology Noise measurement Prognostics and health management

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