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Vibration-induced bias error reduction using loop gain compensation for high-precision fiber optic gyroscopes

Journal of Systems engineering and Electronics 2025年第1期36卷 224-232页

作者： CHEN Heyu QIN Xuexin XIE Huan KONG Linghai ZHENG Yue School of Instrumentation Science and Opto-Electronics Engineering Beihang UniversityBeijing 100191China Beijing Institute of Computer Technology and Application Beijing 100854China Hunan Shuaiwei Control Technology Co. LTDChangsha 421000China Beijing Institute of Control and Electronic Technology Beijing 100038China

Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

关键词： vibration-induced error fiber optic gyroscope(FOG) loop gain

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Fog Computing in IoT: Architecture and applications 9

Fog Computing in IoT: Architecture and Applications

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Agrawal, Anurag Vijay Shashi Gowridurga, A. Suresh Balakrishnan, T. Shaikh, Anwar Ahamed Kalaipriya, V. J. P. Institute of Engineering And Technology Department of Electronics And Communication Engineering Uttar Pradesh Meerut250001 India Ccs University Department of Computer Application Meerut250004 India Velammal Institute of Technology Department of Computer Science And Engineering Tamil Nadu 601204 India Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Thandalam602105 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India

ISBN: (纸本)9798350365092

This exploration paper explores the paradigm of Fog Computing as a transformative armature in the Internet of effects(IoT) geography. Fog Computing represents a decentralized approach, positioning computing coffers closer to the edge of the network, thereby addressing quiescence, bandwidth, and data sequestration challenges current in traditional polycentric IoT infrastructures. The paper provides an in-depth analysis of Fog Computing's armature, expounding its crucial factors similar to fog bumps, middleware, and edge bias. likewise, it investigates the different operations of Fog Computing in IoT, ranging from realtime data analytics, effective resource operation, to enhanced security and sequestration measures. By bridging the gap between edge bias and centralised pall waiters, Fog Computing emerges as a vital result, fostering the elaboration of scalable, effective, and responsive IoT ecosystems. This exploration contributes precious perceptivity to the architectural nuances and practical operations, paving the way for the integration of Fog Computing as a foundation in the ever-expanding realm of IoT technologies. © 2024 IEEE.

关键词： Fog computing

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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Istanbul34467 Turkey Wingie Enuygun Group Istanbul34750 Turkey University of Bakirçay Department of Computer Engineering Izmir35665 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34467 Turkey ITU Ari Teknokent Parton Big Data Analytics and Consulting Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI's ChatGPT 4o and ChatGPT 4o-mini, Anthropic's Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google's Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI's ChatGPT and Anthropic's Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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SVR: Feature Selection Based Support Vector Regression for Humidity Data Prediction 5

SVR: Feature Selection Based Support Vector Regression for H...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Bishnupriya Mantri, Jibendu Kumar Ray, Niranjan Kumar Mscb University Department of Computer Application India Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

Weather data like humidity is a key indicator in meteorology which is influenced by multiple climatic factors. Higher humidity levels become a threat to human health, cause storms and also lead to increase in rainfall, temperature and air borne pollutants. Therefore, prediction of humidity can be helpful in taking necessary action plans for protection of both human life and environment. Weather data can be predicted by multiple regression methods using explanatory variables. However, the effectiveness and prediction accuracy of the multiple regression methods are affected by the irrelevant or non-informative features and the uncertainties lie with the predictions. Feature selection (FS) methods can improve the effectiveness of prediction models and also reduce the uncertainties of predictions by reducing the dimensionality of the dataset by selecting more explanatory variables. On this basis, we have proposed feature selection-based support vector regression (SVR). The proposed hybrid model incorporates Random Forest (RF) model-based FS method in SVR to predict the future humidity data. Performance of RF model-based FS method is compared with two other feature selection methods like correlation-based method and recursive feature elimination (RFE) method. The prediction accuracy of the SVR model is compared with two other multiple regression models: artificial neural network (ANN) regression and decision tree regression (DTR). © 2025 IEEE.

关键词： Multiple linear regression

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Determining human-coronavirus protein-protein interaction using machine intelligence

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Medicine in Novel Technology and Devices 2023年第2期18卷 123-142页

作者： Arijit Chakraborty Sajal Mitra Mainak Bhattacharjee Debashis De Anindya J.Pal Bachelor of Computer Application Department The Heritage AcademyKolkataIndia Department of Computer Science and Engineering Heritage Institute of TechnologyKolkataIndia Department of Biotechnology Heritage Institute of TechnologyKolkataIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataIndia University of Burdwan BurdwanIndia

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral ***,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical *** article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based *** classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this *** proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment ***,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

关键词： Coronavirus Ensemble learning Machine intelligence Protein-protein interaction

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A Survey on Green IoT and Its Opportunities for Future Directions

A Survey on Green IoT and Its Opportunities for Future Direc...

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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Upadhye, Sachin Shelke, Nilesh Thakur, Uma Patel Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350380880

The Internet of Things (IoT) has changed the way technology has evolved, integrating it with other aspects such as home appliances for home automation, smart city projects, e-waste management, and many more. But with the increase in the usage of IoT, there are several challenges that come along, like an increase in energy consumption. Due to this, the Green IoT comes into play. It looks for alternative solutions to the present style of implementing IoT technology, which leads to eco-friendly, sustainable, and cost-effective solutions. This survey points towards effective ways of implementing the IoT for eco-friendly, sustainable, and cost-effective management. The Internet of Things (IoT) has transformed the way technology is integrated into various aspects of life, including home automation, smart city projects, and e-waste management. However, the increasing usage of IoT technology has led to challenges, such as higher energy consumption. This has given rise to Green IoT, which seeks alternative solutions for implementing eco-friendly, sustainable, and cost-effective IoT technology. This survey focuses on effective ways to implement IoT technology for sustainable and eco-friendly cost management. It also explores the potential of IoT technology in new areas, its future sustainability, and its challenges. The adoption of green IoT practices can reduce pollution hazards, traffic waste, and energy consumption while enhancing public safety and quality of life. To create sustainable and eco-friendly cities, researchers, policymakers, and industry leaders must collaborate to address IoT implementation challenges. The survey concludes by highlighting opportunities for future research and innovation in this field. © 2023 IEEE.

关键词： Internet of things

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How do benefits and challenges influence students’ satisfaction with online education? The mediation effect of awareness

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International Social Science Journal 2024年第252期74卷 299-318页

作者： Chawla, Udit Sondhi, Sundeep Singh Jabbar, Meerja Akhil Mishra, Varsha Singh, Harsh Vikram Department of Business Administration University of Engineering & Management Kolkata India Department of CSE(AI & ML) Vardhaman College of Engineering Hyderabad India Digital Marketing Royal Holloway University of London Egham United Kingdom Computer Application Techno India University Kolkata India

Traditional education is shifting towards online instruction, and the temporary closure of educational institutions due to COVID-19 has prompted its dramatic increase in the last couple of years. With additional benefits, such as flexible schedule, low cost and anywhere learning, online education will determine the near future of education. Due to the increase in Internet reach in India, face-to-face instructional methods to which students are habituated to can be substituted. This study aims to analyse students’ satisfaction with the parameters of awareness, challenges and benefits of using new technologies to learn online. With the help of a structured questionnaire and interactions with local inhabitants, the researchers obtained 430 samples from the outskirts of five different cities (Kolkata, Mumbai, Delhi, Chennai and Bengaluru) in India. A conceptual model was built around the constructs of awareness, challenges, benefits and student satisfaction, and their relationship was established and empirically tested. It is found that factors like challenges, benefits and awareness have a significant influence on student satisfaction with online learning. We also find that awareness helps in decreasing the challenges faced and increasing the value of benefits of online education for students. © 2023 John Wiley & Sons Ltd.

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Efficient opportunistic routing with social context awareness for distributed mobile social networks

Efficient opportunistic routing with social context awarenes...

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作者： Xu, Fang Xiao, Nan Deng, Min Xie, Yong Xiong, Zenggang Xu, Qiong School of Computer and Information Science Hubei Engineering University Xiaogan China Department of Computer Technology and Application Qinghai University Xining China

Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction-based social-aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete-time semi-Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state-of-the-art algorithms. © 2019 John Wiley & Sons, Ltd.

关键词： Forecasting

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Inculcating Algebra Properties in the Context of Image Processing

Inculcating Algebra Properties in the Context of Image Proce...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agarwal, Trapty Raja Praveen, K.N. Asif Iqbal, Mohammad Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Computer Science Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350357769

Strong detection with mitigation systems are crucial given the growing danger of computer cluster breaches and worldwide assaults. Given the rising frequency and magnitude of network assaults and abnormalities, the intrusion detection systems (IDS) is an essential tool for recognizing them. In this study, we provide a novel method for security breach detection using three different DNN (deep neural network) vision-based approaches. The classification process uses each of the four parts (Red, Colored, Aquamarine's and Alpha) or circuit-generated pictures as a foundation, making it easier to train and validate the reputable ResN et50 visual analysis framework. We assess the performance of our suggested approach on two well-known datasets: UNSW-NB15 and Regularly Utilize Management Defeat of Service (DoS). The results demonstrate our method's outstanding effectiveness, as it identified broad exploits in the UB test with a 99.8% success rate. Moreover, our method achieves 99.7% accuracy in detecting devices unauthorized to enter restricted areas and diagnosing comparable intrusion data pertaining to cars. With this effort, cybersecurity defenses against emerging attacks have taken a major step forward. © 2023 IEEE.

关键词： DLN Firmware and Image Classification IDS NTF ResNet 5.0 UB Test UNSW-NB15 Zip Files

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AI-Enabled Learning for Imbalanced Class Data using Spark Streaming Concepts 21

AI-Enabled Learning for Imbalanced Class Data using Spark St...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Tiwari, Sadhana Zohair, Mohammad Chandra, Ritesh Agarwal, Sonali Agarwal, Parikshit Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Jamia Millia Islamia Department of Computer Engineering New Delhi India SIET Department of Computer Application Prayagraj India

ISBN: (纸本)9798350358230

Chronic Diseases are a very significant problem around the world. Manhandling medical cases often leads to significant repercussions in the treatment process and needs to be detected and dealt with early on. Unusual clinic actions are often related to medical errors, enabling detection and alerting. Huge leaps in machine learning, data science, and big data analytics techniques have led to innovative ways to deal with these issues. As a result, mortality rates can be greatly reduced in all sorts of medical cases. In this work, a new ensemble method is proposed for multiclass classification applied on the US-Chronic Disease Indicator (US-CDI) dataset and validated using UCI-Diabetes data by applying K-means clustering to it to detect chronic disease. The proposed hybrid model, KD-Boost Ensemble, is based on the concept of ensemble voting mechanisms, which combine the predictions of the K-neighbors classifier, Decision Tree classifier, and Gradient Boosting classifier to obtain improved accuracy over the dataset when compared to the individual conventional classifiers. This chronic disease classification achieved 96% accuracy using the proposed model. The devised model is validated through the diabetes dataset in a real-time environment and attains 98% accuracy. © 2023 IEEE.

关键词： Decision trees

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