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Business intelligent connection between quality and evaluation methods: a case study

International Journal of Applied Systemic Studies 2022年第4期9卷 330-343页

作者： Mandellos, Georgios Tsipianitis, Dimitris Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Nowadays, information and knowledge represent the fundamental wealth of an organisation. Enterprises try to utilise this wealth to gain advantage when making decisions such as a project’s acceptance. After a project finishes, a quality management plan defines its quality level. The business intelligence theories help the individual to evaluate the proposed plan in order to target the project’s results. But, do all the evaluation methods have the same reliability? We check the project’s quality by using paper and online questionnaire administration. Although the scope is the same, there are differences, giving different results. So, we intelligently investigated the quality of the university educational process. The results show that the administration method has a significant effect on overall results. Thus, the quality check of a project using questionnaires needs to have an attentive design, avoid points that introduce uncertainties and able to show the real opinion of the respondents. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Surveys

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The value of formative evaluation in an education program

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International Journal of Applied Systemic Studies 2022年第4期9卷 381-388页

作者： Tsipianitis, Dimitris Mandellos, Georgios Systems and Automatic Control Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece Telecommunications and Information Technology Division Electrical and Computer Engineering Department University of Patras RionGR-26504 Greece

Evaluation is a process that compares the results with the target, an initial situation with the final one. ‘Evaluate’ means making an assessment based on some measure or information and referring to some criteria. The Formative evaluation is a process which is used by trainers and trainees and provides feedback on updating current teaching and learning methods in order to improve trainer’s achievement in relation of teaching goals. In this case study a research was carried out using the technique of formative evaluation in order to evaluate two thematic areas with the cooperation of the program’s leaders. The aim of the study was to extract conclusions and opinions about the program’s value, the achievement goals and the organisation implementation requirements and how all this results will been feed backed to program managers and policymakers with the appropriate information which are necessary to adopt appropriate corrective interventions for programs quality improvement. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Quality control

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Reference Model Design in Multiple Timescale Adaptive control

Reference Model Design in Multiple Timescale Adaptive Contro...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

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Reinforcement Learning-Based Output Stabilization control for Nonlinear systems With Generalized Disturbances 25

Reinforcement Learning-Based Output Stabilization Control fo...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Tang, Zezhi Rossiter, J Anthony Dong, Yi Panoutsos, George University Of Sheffield Department Of Automatic Control And Systems Engineering SheffieldS13JD United Kingdom University Of Southampton Department Of Electronics And Computer Science Southampton United Kingdom

ISBN: (纸本)9798350340266

This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals;so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme. © 2024 IEEE.

关键词： Nonlinear systems

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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Secure-by-Construction Synthesis for control systems

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IEEE Transactions on Automatic control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Robust Car-Following control of Connected and Autonomous Vehicles: A Stochastic Model Predictive control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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Distributed Kalman Filtering for Temporal Resolution Enhancement of Thermal Images

Distributed Kalman Filtering for Temporal Resolution Enhance...

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International Symposium on Image and Signal Processing and Analysis

作者： Sabeethan Kanagasingham Andrew R. Mills Visakan Kadirkamanathan Department of Automated Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

This paper presents a novel method for enhancing temporal resolution of thermal image sequences. The proposed approach employs the Kalman filter, which exploits the spatio-temporal correlation of the underlying physical processes and leverages temporal information contained in rows of an image captured via rolling shutter readout, to successfully reconstruct intra-frame sequences. To address the computational complexity associated with large-scale Kalman filtering, we propose a distributed filtering scheme that takes advantage of the neighbourhood relationship in the underlying diffusion process. Simulation results demonstrate the capability of the method in enhancing temporal resolution while achieving acceptable Peak Signal-to-Noise Ratio (PSNR) of approximately $30dB$ . The distributed Kalman filter offers significant reductions in computational and storage costs, making it highly scalable for large images.

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AggFollower: Aggressiveness Informed Car-Following Modeling

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Chen, Xianda Yuan, Xinyi Zhu, Meixin Zheng, Xinhu Shen, Shaojie Wang, Xuesong Wang, Yinhai Wang, Fei-Yue Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China School of Transportation Engineering Tongji University Shanghai China Department of Civil and Environmental Engineering University of Washington Seattle WA USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Car-following is the most common driving scenario where a following vehicle follows a lead vehicle in the same lane. One crucial factor of car-following behavior is driving style which affects speed and gap selection, acceleration pattern, and fuel consumption. However, existing car-following research used limited categories of driving style through pre-defined patterns and failed to encode driving style into data-driven car-following models. To address these limitations, we propose the Aggressiveness Informed Car-Following (AICF) modeling approach, which embeds driving style as a dynamic input feature in data-driven car-following models. In detail, We design driving aggressiveness tokens using four physical quantities (jerk, acceleration, relative speed, and relative spacing) to capture the heterogeneity of driving aggressiveness. These tokens were then embedded into a physics-informed Long Short-Term Memory (LSTM) based car-following model for trajectory prediction. To evaluate the effectiveness of our approach, we conducted extensive experiments based on 12,540 car-following events extracted from the HighD dataset and 24,093 events from the Lyft dataset. Compared to models devoid of considerations for driving aggressiveness levels, AICF exhibits superior efficacy in mitigating the Mean Square Error (MSE) of spacing and collision rate. To the best of our knowledge, this is the first work to directly incorporate real-time driving aggressiveness tokens as input features into data-driven car-following models, enabling a more comprehensive understanding of aggressiveness in car-following behavior. IEEE

关键词： Long short-term memory

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Implementation of Base Components of Neuro-like Cryptographic Data Protection systems on FPGA 13

Implementation of Base Components of Neuro-like Cryptographi...

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13th IEEE International Conference on Electronics and Information Technologies, ELIT 2023

作者： Tsmots, Ivan Rabyk, Vasyl Tkachenko, Roman Opotyak, Yurii Teslyuk, Vasyl Department of Automated Control Systems Lviv Polytechnic National University Lviv Ukraine Department of Radiophysics and Computer Technologies Ivan Franko National University of Lviv Lviv Ukraine Department of Publishing Information Technologies Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798350383096

The implementation of the base components of neuro-like cryptographic data protection systems using FPGA is considered. The structure of the data encryption module using polynomials based on a neuro-like network was developed. Module implemented with the help of VHDL in Quartus II development environment. The base components of different dimensions, which are part of the encryption module, have been implemented. The components allow quick implementation of encryption modules corresponding to different neural network architectures. Modeling of the module's operation and evaluation of the encryption time, hardware resources required for its implementation, and the implementation of the components included in the module were carried out. To prepare the parameters of a neural network of a given architecture, which are used in the implementation of the basic encryption operation, the Training_NS_MGT and Transfor_Data programs were implemented in the C language on the Raspberry Pi 4 microcomputer. Examples of the formation of matrices of weighting coefficients by these programs are given. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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