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Cloud ERP systems architectural challenges on cloud adoption in large international organizations: A sociomaterial perspective

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Procedia Computer Science 2023年 219卷 797-806页

作者： Victoria Paulsson Björn Johansson Department of Management and Engineering Division of Information Systems and Digitalisation SE-581 83 Linköping Sweden

This combined literature review and case employs a sociomaterial perspective to explore architectural challenges and countermeasures that BioOrganism, the case company, should consider in a decision to adopt a cloud enterprise resource planning (ERP) system. The case has experiences with an on-premises model, but not the cloud model per se. The study is designed with an element of case to provide a solid backdrop toward the sociomaterial perspective, which focuses on a reciprocal enactment between the cloud ERP systems architecture and the BioOrganism contextual surrounding. Six architecture challenges were identified: (1) data security, (2) data privacy, (3) network dependency, (4) system mobility, (5) vendor lock-in, and (6) scalability. Countermeasures that BioOrganism, and other large international organizations of similar nature, can take to mitigate risks from the six challenges are presented in the review.

关键词： Cloud architecture Cloud ERP systems Literature review Sociomaterial perspective

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X-FMAS Beamforming Method for 3D Imaging Using Row-Column Addressed Array

X-FMAS Beamforming Method for 3D Imaging Using Row-Column Ad...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Sun, Qiandong Fu, Yapeng Xu, Kailiang Fudan University Center for Biomedical Engineering School of Information Science and Technology Shanghai China Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China

ISBN: (纸本)9798350346459

Row column addressed (RCA) array have been considered as an efficient solution for ultrafast 3D ultrasound imaging in large field of view with significant reduction of channels. Due to lacking of focusing process, the coherent compounding of orthogonal plane wave (OPW) was developed for RCA array to obtain isotropic point spread function (PSF) by transmission with the rows and orthogonal reception with the columns or vice versa. In this work, we proposed X-FMAS beamforming technique, a method based on the spatial coherence of the complementary image frames and orthogonal reception apertures of RCA probe, to obtain lower side-lobe level of PSF and then higher contrast-to-noise ratio (CNR) of ultrasound images. The simulated results of PSF show that the X-FMAS technique provides a 35dB reduction of side-lobe level compared with OPW, and it was found in phantom experiments that 10dB and 4dB gain of CNR of power Doppler images compared with OPW and XDoppler can be obtained using the X-FMAS method. © 2023 IEEE.

关键词： Beamforming

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Application Research of Landmarks in Path Planning Algorithms of Mobile Robots 2

Application Research of Landmarks in Path Planning Algorithm...

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2nd International Conference on Digital Society and Intelligent systems, DSInS 2022

作者： Fan, Linghao Liu, Xiangde Zhang, Yi Chongqing University of Posts and Telecommunications Intelligent Systems and Robotics Laboratory Chongqing China Chongqing Information Accessibility and Service Robot Engineering Technology Research Center Chongqing China

ISBN: (纸本)9781510663244

A way of incorporating landmark information matrix QR to affect the global path planning algorithm is proposed for the use of landmarks in robot localization and navigation. It is also investigated how this method affects the pace at which the original algorithm solves problems. The reward and punishment items in this technique include dangerous locations with lots of obstacles around them, landmarks with location information connected to the ground, and the two combined. Three representative common robot path planning algorithms are also imposed as influences. Through MATLAB simulation experiment, find the optimal path planning algorithm that is appropriate for applying reward and punishment by comparing the quality of the robot's driving path and the algorithm solution speed before and after the reward and punishment. According to the simulation results, the reward and punishment approach is practical and efficient. The heuristic search algorithm is better suited for reward and punishment, and both its path quality and speed of solution have greatly increased. © 2023 SPIE.

关键词： Genetic algorithms

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Practical Numerical Modeling Approach for a Design of a Two-Dimensional Linear Synchronous Actuator 14

Practical Numerical Modeling Approach for a Design of a Two-...

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14th International Symposium on Linear Drivers for Industry Applications, LDIA 2023

作者： Ito, Noriaki Koseki, Takafumi Ohnishi, Wataru Nakamura, Yuichiro Takahashi, Kenji Sekiguchi, Hiroyuki The University of Tokyo Dept. of Electrical Engineering and Information Systems Tokyo Japan Advanced Technology R&d Center Mitsubishi Electric Corporation Hyogo Japan

ISBN: (纸本)9798350329612

This paper proposes the design of a magnetically levitated planar motor, the configuration of a basic numerical method to embody the control concept, and the design procedure for an experimental test rig. This motor is stable in the levitating direction but unstable in the driving direction because it utilizes the repulsive force between the stator permanent magnets and the moving coils. One problem with magnetically levitated planar motors is the time-consuming nature of the three-dimensional finite element analysis. This analysis requires significant computational resources and can result in significant delays in the design process. To address this problem, a less time-consuming method using Gaussian process regression is presented. In addition, the proposed analysis is verified using a test rig equipped with several sensors. These sensors will provide measurements of static force characteristics and flux linkage to evaluate the performance and effectiveness of the motor under various operating conditions. © 2023 IEEE.

关键词： Finite element method

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EnergyCIDN: Enhanced Energy-Aware Challenge-Based Collaborative Intrusion Detection in Internet of Things 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Li, Wenjuan Rosenberg, Philip Glisby, Mads Han, Michael Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong Department of Electronic Systems Aalborg University Aalborg Denmark KOTO Research Center China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With cyber attacks becoming more complex and advanced, a separate intrusion detection system (IDS) is believed to be insufficient for protecting the whole computer networks. Thus, collaborative intrusion detection networks (CIDNs) are proposed aiming to improve the detection performance by allowing various nodes to share required information or messages with other nodes. To defeat insider threats during the sharing process (e.g., malicious information), trust management is a necessary security mechanism for CIDNs, where challenge-based CIDNs are a typical example that sends a special kind of message, called challenge, to evaluate the reputation of a node. The previous work has proven that challenge-based CIDNs can defeat most common insider threats, but it may still suffer from some advanced insider threats, e.g., passive message fingerprint attack (PMFA). In this work, we develop EnergyCIDN, an enhanced challenge-based CIDN by adopting an energy-aware trust management model against advanced insider attacks. In the evaluation, we study the performance of EnergyCIDN under both simulated and practical Internet of Things (IoT) environments. The results demonstrate that EnergyCIDN can perform better than many similar schemes in identifying advanced malicious nodes. © 2023, Springer Nature Switzerland AG.

关键词： Energy utilization

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LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection 25

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Jiaxing Huang, Shaohan Luan, Zhongzhi Yang, Shu Fung, Carol Yang, Hailong Qian, Depei Shang, Jing Xiao, Zhiwen Wu, Zhihui Sino-German Joint Software Institute Beihang University Beijing100191 China The Concordia Institute for Information Systems Engineering Concordia University Quebec Canada The China Mobile Information Technology Center China

ISBN: (纸本)9798350330014

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task. © 2023 IEEE.

关键词： Anomaly detection

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Distributed Fuzzy Adaptive Fault-Tolerant Consensus Control for Nonlinear Multi-Agent systems with Sensor Faults and Actuator Faults 43

Distributed Fuzzy Adaptive Fault-Tolerant Consensus Control ...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Xinnan Wang, Yunguo Ma, Huan Liu, Yuanchao Sun, Fan Yu, Feng Liu, Jianchang Northeastern University The College of Information Science and Engineering Shenyang110819 China Northeastern University The National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Co. Ltd Benxi117017 China Shenyang University of Chemical Technology The College of Information Engineering Shenyang110142 China

ISBN: (纸本)9789887581581

This paper investigates the output consensus problem for a class of nonlinear multi-agent systems (NMAS) with actuator and sensor faults. Firstly, a novel structural design method, combining parameter separation and function recombination, is introduced to address the challenges arising from multiple sensor faults. Secondly, a fuzzy logic system (FLS) is employed to approximate unknown functions. Then, a second-order tracker differentiator (TD) is used to eliminate the explosion of complexity in backstepping. Subsequently, the Nussbaum function is utilized to address the unknown control orientation. Based on backstepping technique, an adaptive fuzzy fault-tolerant consensus control (FTCC) method is proposed to remove the difficulties of sensor and actuator faults. It is confirmed that all signals in the closed-loop NMAS are bounded, ensuring the convergence of the output consensus error to an arbitrarily small neighborhood of the origin. Finally, the simulation experiment verifies the efficacy of the proposed controller. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Backstepping

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Understanding Drivers' Emotion Variations During NDRTs in Conditional Automated Driving Using EEG Data 7

Understanding Drivers' Emotion Variations During NDRTs in Co...

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7th IEEE International Conference on Transportation information and Safety, ICTIS 2023

作者： Guo, Wenhui Zhang, Hui Ding, Naikan Liu, Yongjie Wuhan University of Technology Intelligent Transportation Systems Research Center Hubei Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Hubei Wuhan430063 China

ISBN: (纸本)9798350308532

Drivers' emotion is one of the key factors affecting driving behavior, negative emotion would be harmful to safe driving. In conditional automated driving, drivers would devote the majority of the time to Non-Driving Related Tasks (NDRTs), and varying NDRTs may lead to variations in drivers' emotions. In this study, a series of conditional automated driving simulation experiments considering four typical NDRTs was conducted, and the electroencephalogram (EEG) signals were detected to identify and characterize drivers' emotions by the valence-arousal (V-A) model, where in valence ranges from displeasure to pleasure and arousal ranges from relaxation to excitement. The results show that 1) the drivers' emotion, as measured by the valence and arousal, was significantly affected by the rest and reading tasks during conditional automated driving, but not influenced by the monitoring vehicle operation and watching video tasks;2) the V-A model revealed that the drivers were prone to keep alert in automated driving while showing increased stress in the rest state. The findings of this study contribute to a better understanding of drivers' emotions and NDRTs in conditional automated driving. © 2023 IEEE.

关键词： Electroencephalography

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An epidermal electrode based triboelectric walking energy harvester for wearable wireless sensing applications

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Science China(Technological Sciences) 2024年第3期67卷 949-957页

作者： CHANG ShiYuan LIU FuHai CHEN JinKai XIA LianBin ZHOU Hao JIANG JingYang DONG Kang ZHANG ChenHao WU Yun CHEN Jin XUAN Weipeng SUN LingLing JIN Hao DONG ShuRong LUO JiKui Ministry of Education Key Laboratory of RF Circuits and Systems Hangzhou Dianzi UniversityHangzhou 310018China Special Equipment College Hangzhou Vocational&Technical CollegeHangzhou 310018China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 330027China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311200China

Harvesting bio-kinetic energy using a triboelectric nanogenerator(TENG) is one of the promising routes to solve the sustainable energy supply problem for wearable electronics. However, additional materials, complex fabricating processes or specific mechanical structures are needed for existing TENGs to harvest bio-kinetic energy. Besides, they need to be tightly attached to the human body, which may result in detachment and malfunction under tense human motion. Herein, an intrinsic epidermal electrode-based TENG(E-TENG) is proposed to harvest human walking energy. The wearing shoes and ground are used as tribo-materials, and the human epidermis is used as the back electrode of the E-TENG. Compared with the traditional TENGs,the E-TENG does not need any additional tribo-materials and complex mechanical structures. Under optimal conditions, the voltage output of E-TENG can reach 914 V. E-TENG has been used as a self-powered warning sensor and pedometer sensor for demonstration. Furthermore, E-TENG based self-powered wireless sensor system has been developed using a newly designed micro energy electronic switch(MEES). Ambient ultraviolet intensity, temperature and humidity information can be monitored and then transmitted to mobile phone every 3.5 min, demonstrating great potential for widespread wearable applications.

关键词： triboelectric nanogenerator wearable wireless sensing self-powered

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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Computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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