System Output Research Articles

Quantized attack-resilient control for Markov jump systems with sensor and actuator attacks

With the networking of control systems, cyberattacks on industrial control systems are becoming more and more frequent. In this study, the quantized attack-resilient control problem for Markov jump systems with sensor and actuator attacks and partly unknown transition rates was investigated. In the existing work, the forms of attack signals are mostly assumed directly. In order to release these assumptions, the mode-dependent monitor and logarithmic quantizer are designed to obtain the forms of attack signals and their constraints. Based on the constraints of attack signals, an adaptive filter is proposed to estimate the real system outputs, which are attacked by cyberattacks. Then, the adaptive compensator and quantized attack-resilient controller are designed to guarantee stochastic stability and [Formula: see text] performance. Benefiting from the quantized attack-resilient control strategy, the proposed method can well combat the attacks, not only reducing the impact of the attacks, but also constraining the attack signals. Finally, an application example is presented to illustrate the obtained results.

Complexity Analysis and Control of Output Competition in a Closed-Loop Supply Chain of Cross-Border E-Commerce Under Different Logistics Modes Considering Chain-to-Chain Information Asymmetry

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state.

On the Consistent Classification and Treatment of Uncertainties in Structural Health Monitoring Applications

Abstract In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of structural dynamics, system identification, and structural health monitoring (SHM), with a primary focus on the latter. Utilizing the classical input–output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability (EOV), virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM.

Clouds on the horizon: clinical decision support systems, the control problem, and physician-patient dialogue.

Artificial intelligence-based clinical decision support systems have a potential to improve clinical practice, but they may have a negative impact on the physician-patient dialogue, because of the control problem. Physician-patient dialogue depends on human qualities such as compassion, trust, and empathy, which are shared by both parties. These qualities are necessary for the parties to reach a shared understanding -the merging of horizons- about clinical decisions. The patient attends the clinical encounter not only with a malfunctioning body, but also with an 'unhomelike' experience of illness that is related to a world of values and meanings, a life-world. Making wise individual decisions in accordance with the patient's life-world requires not only scientific analysis of causal relationships, but also listening with empathy to the patient's concerns. For a decision to be made, clinical information should be interpreted considering the patient's life-world. This side of clinical practice is not a job for computers, and they cannot be final decision-makers. On the other hand, in the control problem users blindly accept system output because of over-reliance, rather than evaluating it with their own judgement. This means over-reliant parties leave their place in the dialogue to the system. In this case, the dialogue may be disrupted and mutual trust may be lost. Therefore, it is necessary to design decision support systems to avoid the control problem and to limit their use when this is not possible, in order to protect the physician-patient dialogue.

An innovative hybrid model combining informer and K‐Means clustering methods for invisible multisite solar power estimation

AbstractThe employment of behind‐the‐meter solar photovoltaic (PV) systems has gained increasing popularity in recent years as more individuals and organizations aim to reduce their reliance on conventional grid‐connected power sources and take advantage of the environmental and economic benefits of solar power. However, precisely estimating the potential output of PV systems is a challenging task, since most of the PV systems used in residential properties have been installed behind the meter. Consequently, electric power companies are limited to accessing only the recorded net electricity consumption. This article introduces an innovative approach to estimate behind‐the‐meter PV power generation within a large region, utilizing a limited representative subset. The proposed framework integrates Missforest, that is, a robust tool for missing data imputation, with a hybrid application of K‐Means, Pearson Correlation Coefficient, and Principal Component Analysis, for the precise selection of representative PV sites. Additionally, it leverages the Informer model, a cutting‐edge deep learning‐based time series model, to link the relationship between the PV power generation at representative sites and the total PV power output on the entire region. To conduct a case study, the power output of 367 PV sites and solar radiation measured at 105 weather stations in Taiwan were collected and analyzed. The application of this comprehensive methodology demonstrates a notable advancement in the estimation of “invisible” PV power generation in comparison to other established techniques.

Enhanced model predictive control using artificial neural networks for grid-connected two-level inverters

Integrating artificial intelligence (AI) to improve the performance of photovoltaic systems is compelling for two main reasons. First, AI technologies are making significant progress and are widely applied across various fields, such as medicine and agriculture. Second, the world is experiencing an increasing demand for energy while simultaneously facing the challenge of reducing reliance on traditional energy sources that exacerbate carbon emissions and climate change. This emphasizes the critical importance of transitioning to sustainable and clean energy solutions. In this context, photovoltaic panels have emerged as one of the most promising and widely adopted renewable energy sources. As the global reliance on solar energy grows, enhancing the efficiency and reliability of solar energy systems has become imperative. However, this transition is accompanied by significant challenges, particularly in integrating these systems with existing power grids. The complexities involved in achieving seamless integration, maintaining consistent performance, and preventing system failures necessitate the adoption of advanced technologies. These challenges are further exacerbated by the need to manage fluctuations in energy production, optimize energy storage, and ensure the long-term sustainability of solar power. This paper presents a novel approach for controlling ANN-MPC solar inverters, leveraging artificial neural networks (ANN) to address the challenges related to the efficiency of photovoltaic systems and their integration with the grid, ultimately reducing dependence on conventional energy sources. Initially, the ANN-based model is trained using large datasets derived from the system inputs and outputs, taking into account all critical operating conditions, such as sudden changes in current. Following the training process, the model acquires extensive data, enabling it to adapt to all transient conditions the system may encounter. The model continuously evolves to improve the efficiency of grid-connected photovoltaic systems. Additionally, experimental validation and performance testing of the proposed model are conducted to verify the effectiveness and flexibility of the ANN-based approach. The experimental results confirm the proposed approach's ability to enhance the performance of grid-connected electrical systems, reduce computational costs, and achieve model-free control.

Operation Optimization Framework for Advanced Reactors Using a Data-Driven Digital Twin

Abstract To meet future energy demand, while producing safe, reliable, and carbon-free energy, nuclear reactors will be needed. To make next-generation reactors more economically competitive, the Artificial Intelligence and Machine Learning-based operation optimization module of the dynamic operation and maintenance optimization (DyOMO) framework is proposed. The operation optimization module of DyOMO consists of a data-driven digital twin coupled to a genetic algorithm (GA) optimizer to quickly and efficiently search the solution space for optimal control schemes. The digital twin consists of a Bayesian Network (BN) known as MVCBayes to incorporate uncertainty in the optimization and feedforward neural networks (FFNN) as MVCNet with GA to conduct the optimization. Over time as reactor systems are operating, component degradation will cause the system's electrical output to decrease or be shutdown entirely to perform maintenance. To prevent this, the DyOMO operation optimization module aims to prolong system operation until the next scheduled maintenance period using multiple variable control (MVC) that simultaneously perturbs all actuators to better control the reactor. Comparing this approach with traditional single variable control (SVC), MVC can extend reactor operation past 5% degradation while SVC begins to struggle once the pump and turbine degradation surpasses 0.85% for load-following (LF) operation. Given this extra operation time, the system can continue to run while maximizing its safety margin until the next scheduled shutdown and potentially decrease the total number of maintenance actions throughout the license period to decrease operational and maintenance (O&M) costs.

A Simulation study of the output characteristics of freestanding Triboelectric nanogenerators with interdigitated electrodes for self-powered sensing application

Diverse forms of mechanical energy are available in environmental routine activities. These energy forms can be harvested, measured and converted to produce electricity at nanogenerator (NG) and micro-scale levels based on the phenomenon of triboelectrification. These motivate this work to develop a self-powered sensing device to detect and monitor static and dynamic processes associated with mechanical activities. The significance is to study the output characteristics of freestanding mode triboelectric nanogenerator (TENG) with multiple units of metal and dielectric electrodes. The integrated modeling environment of COMSOL Multiphysics software was employed for the simulation study of the proposed TENG. The system output responses considered for analysis includes, open circuit electric potential and short circuit surface charge density. For the open circuit, the electric potential was achieved at maximum value of 10kV, while for short circuit surface charge density, the electric potential was achieved at maximum value of 27 Cm-2. The study results revealed that the input parameter of contact displacement of electrodes is proportional to the output electrical potential of the system. Hence, the efficiency of TENG can be deployed for energy harvesting and sensing of mechanical variables.

Critical Observability Enforcement in Discrete Event Systems Using Differential Privacy

In the context of discrete event systems (DESs), critical states usually refer to a system configuration of interest, describing certain important system properties, e.g., fault diagnosability, state/language opacity, and state/event concealment. Technically, a DES is critically observable if an intruder can always unambiguously infer, by observing the system output, whether the plant is currently in a predefined set of critical states or the current state set is disjointed with the critical states. In this paper, given a partially observable DES modeled with a finite-state automaton that is not critically observable, we focus on how to make it critically observable, which is achieved by proposing a novel enforcement mechanism based on differential privacy (DP). Specifically, we consider two observations where one observation cannot determine whether a system is currently in the predefined critical states (i.e., the observation violating the critical observability) while the other is randomly generated by the system. When these two observations are processed separately by the differential privacy mechanism (DPM), the system generates an output, exposed to the intruder, that is randomly modified such that its probability approximates the two observations. In other words, the intruder cannot determine the original input of a system by observing its output. In this way, even if the utilized DPM is published to the intruder, they are unable to identify whether critical observability is violated.

Adaptive Control of Constrained Nonlinear Systems With Intermittent Faults: A Fixed‐Time Fault‐Tolerant Framework Based on Projection Operator

ABSTRACTThis article investigates the problem of adaptive fixed‐time fault‐tolerant control for nonlinear systems with time‐varying intermittent faults and asymmetric output constraints. For this purpose, a novel adaptive law based on the projection operator technique is developed to estimate time‐varying intermittent faults, which eliminates the problem that the estimated value increases continuously with the accumulation of fault times. Considering that the fault estimation time needs to be less than the minimum time interval of intermittent faults, the fixed‐time design methodology is introduced to ensure that the relationship between the above two can be quantitatively analyzed. Then, a novel adaptive fixed‐time fault‐tolerant control strategy is put forward for the closed‐loop system, in which a universal barrier function is adopted to remain the system output in the time‐varying asymmetric constraint condition invariably. The theoretical analysis indicates that the developed strategy can guarantee the boundedness of all signals in the closed‐loop system in a fixed‐time. Finally, two examples, one of which involves the rotary steerable drilling tool systems are provided to demonstrate the feasibility and validity of the put forward scheme.

Image Contrast Enhancement Using General Histogram Equalization and Homomorphic Filtering

The realm of image processing is characterized by the judicious application of mathematical operations to facilitate image transformation and refinement. By synergizing signal processing techniques, image processing can culminate in an enhanced image or the extraction of salient parameters. This research concentrates on ameliorating the contrast of images beset by low contrast, concomitantly extracting relevant image parameters. Images with subpar contrast can engender flawed outcomes in myriad disciplines, highlighting the necessity of contrast enhancement. This study introduces an innovative image processing system, conducting a comparative analysis of General Histogram Equalization and Homomorphic Filtering. The results unequivocally demonstrate the superiority of Homomorphic Filtering. The system's output manifests pronounced efficiency in elevating image contrast, heralding far-reaching implications.

Expert System for Assessing Anxiety Levels in Toxic Relationships Using the Case-Based Reasoning Method Based On The Web

Experiences in toxic relationships often trigger significant emotional stress and impact mental health disorders. This study aims to develop a web-based expert system using the Case-Based Reasoning (CBR) method to evaluate anxiety levels caused by toxic relationships. The system is designed to provide more specific treatment by accurately analysing patterns of disorders resulting from toxic relationships. The system's development follows the waterfall model. System testing was conducted using the black-box testing method, demonstrating that the system performs as expected. The results of manual calculations were compared with the system outputs and showed consistency. Testing using 300 cases—80% as training data (240 cases) and 20% as testing data (60 cases)—achieved an accuracy of 91.67%. The recommendations provided include initial steps to manage anxiety. This indicates that the CBR method effectively distinguishes anxiety levels based on similar cases. These findings contribute to clinical psychology by providing a technological tool for quickly identifying anxiety levels. For practitioners, this system can serve as an initial reference before further treatment, while for users, it offers easy access to understanding their mental condition. This system is expected to be an innovative solution supporting accessible mental health care.

Life cycle assessment and thermodynamic performance of biomass-based combined cogeneration

Abstract This study presents a thermodynamic and life cycle analysis of a biomass-based integrated energy system, comprising a biomass gasification cycle, a steam Rankine cycle, and an ammonia fluid Organic Rankine cycle. The primary objective of the system is to efficiently convert olive husk, a sustainable biomass source, into electricity and heating. The gasification process generates syngas, which fuels a gas turbine, producing high-temperature exhaust that drives a Rankine cycle. The thermodynamic analysis indicates that the overall system achieves an energy efficiency of 20.85% and an exergy efficiency of 11.70%. The Rankine cycle exhibits an energy efficiency of 23.91%, while the Organic Rankine cycle demonstrates a higher exergy efficiency of 68.87%. Parametric studies reveal that increasing the combustion chamber temperature significantly enhances system efficiency and output, with energy efficiency rising linearly as the temperature increases from 800 °C to 1000 °C. Additionally, the analysis emphasizes the importance of the compressor's compression ratio on system performance. A life cycle assessment conducted using the ReCiPe methodology evaluates the environmental impact of the system throughout its lifecycle, revealing critical insights into its sustainability. This comprehensive evaluation highlights the potential of utilizing agricultural waste, such as olive husk, to produce renewable energy, thereby supporting environmental sustainability and waste management goals. The findings underscore the viability of the proposed system for enhancing energy production while minimizing environmental impacts, making it a promising solution for renewable energy generation in regions with abundant biomass resources.

A novel elliptical MIMO antenna design for enhanced wireless connectivity in greenhouse applications

This paper presents a novel design and development of an elliptical antenna tailored for Greenhouse applications, providing support for multiple wireless services. The proposed antenna configuration is a quad-port multiple input multiple output (MIMO) system consisting of four identical elliptical antenna elements arranged in an orthogonal configuration on a Polycarbonate substrates. It operates effectively across the 2.4 GHz band and the 3–14.8 GHz band, enabling seamless connectivity for a range of Greenhouse and wireless applications, including Bluetooth, Wi-Fi, intelligent control systems (ICS), Greenhouse-to-infrastructure (G2I) communication, sensor networks (SN), and Greenhouse-to-network (G2N) connections. Rigorous analysis of antenna diversity performance is conducted, focusing on key parameters such as the envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL). The antenna demonstrates exceptional performance, with an ECC of less than 0.005, DG exceeding 10 dB, TARC below − 25 dB, and CCL less than 0.25 bits/s/Hz. Detailed parametric analysis confirms the suitability of this design for 5G Greenhouse applications.

Energy conversion efficiency improvement studies on the hydrogen-fueled micro planar combustor with multi-baffles for thermophotovoltaic applications

In order to improve the energy conversion efficiency of micro thermophotovoltaic (MTPV) system, in this work, a micro planar combustor with multi-baffles is designed for thermophotovoltaic applications. Effects of baffle-to-sidewall distance, baffle-to-inlet distance, baffle length and baffle number on the performance of the micro planar combustor are numerically investigated. Results demonstrate that it is better to place the multi-baffles at the middle of combustion chamber for improving the total energy output and total energy conversion efficiency of MTPV system. However, it cannot be ignored that the pressure drop of micro planar combustor is greatly increased. Meanwhile, with the increment of baffle-to-inlet distance and baffle length, the outer wall temperature can be higher and more uniform. Furthermore, when the total length of baffles is a constant, more baffle number is good for obtaining higher outer wall temperature, as well as the total energy output and total energy conversion efficiency of the MTPV system can be improved.

Pembuatan Sistem Pencatatan Pinjaman Kelompok Wanita Tani Berbasis Web Menggunakan Metode V-Model

This research focuses on designing a web-based loan recording system for the Jati Mekar Women Farmers Group (KWT) in Sindanglaut Village. KWT Jati Mekar is an organization consisting of women farmers in Sindanglaut Village who have joined together to improve their welfare and productivity through various agricultural and economic activities.The main objective of the research is to develop a system that can increase efficiency and accuracy in recording group members' loans. The methodology used is the V-Model, which allows for structured and systematic system development through stages of requirements analysis, design, implementation, testing, and maintenance.This system is designed to facilitate the process of recording, monitoring, and reporting loans, as well as increasing transparency in the group's financial management. During the development process, blackbox testing was conducted to evaluate the system's functionality without examining the internal code structure. This testing focuses on the system's inputs and outputs, ensuring that each feature functions according to expected specifications.The research results show that the implementation of this web-based system can address problems that often occur in manual recording, such as inaccurate data and difficulties in tracking loan status. Blackbox testing verifies that the system can handle various usage scenarios correctly, including member data management, loan recording, and report generation.

MINN: Learning the Dynamics of Differential-Algebraic Equations and Application to Battery Modeling.

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalizability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in modeling real-world dynamic systems for optimization and control purposes. We propose a novel machine learning architecture, termed model-integrated neural networks (MINN), that can learn the physics-based dynamics of general autonomous or non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

Performance investigation of multi-functional series-connected building-integrated photovoltaic/thermal system and analysis of its multi-objective optimization design strategy

The series-connected building-integrated photovoltaic/thermal (BIPVT) system can significantly increase the terminal temperature and achieve both power generation and heating. However, its utilization of thermal energy is limited. Airborne infectious diseases threaten indoor occupant health. Pathogenic microorganisms can be thermally inactivated, and the effectiveness of inactivation is positively correlated with exposure temperature and duration. Therefore, series-connected BIPVT system has the potential for air thermal disinfection. Based on this, a multifunctional series-connected BIPVT system that provides heating, power generation, and air purification is proposed. However, these three outputs conflict with each other, and there is a lack of optimized design strategies to maximize the system's overall output. In response, this paper develops a multi-objective optimization strategy for the system and optimizes its design across different climate zones. The main content is as follows: (1) Compared with single-stage systems, series systems can significantly improve air purification performance. The series-connected BIPVT system with the best air purification effect can increase the single-pass inactivation ratio to 100% under the irradiance of 600 W/m2. (2) The glazed photovoltaic/thermal-glazed solar thermal system has the best thermal performance and air purification performance, the thermal efficiency is 38.1% and the clean air delivery rate is 98.6 m3/h under the irradiance of 800 W/m2. (3) Compared to single-objective optimization and non-optimized designs, the multi-objective optimization design achieves the highest technique for order of preference similarity to the ideal solution (TOPSIS) score, which is 0.5968.

RT-SVM: Channel modeling and analysis for indoor terahertz communication scenarios

Considering the increasing demands for wireless communication networks and information system applications, the wireless sector must meet the pressing requirement for high-speed technological advances. The terahertz (THz) frequency band, spanning 0.3 to 10 THz, is of significant interest in current technological innovations and academic research in telecommunications. The THz frequency band has unique properties, including high time-resolving power (femtosecond) and low absorption. This paper proposes a THz propagation ultra-wideband (UWB) channel model and coding scheme for indoor environments starting from 0.3 THz. First, we investigated the propagation path loss model by considering the effects of transmitter dimensions, molecular absorption, and attenuation as functions of frequency and distance. We developed models for power propagation delay, multiple input multiple output (MIMO) systems and discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) response channels. Using the standard Saleh-Valenzuela model combined with Ray-tracing (RT-SVM), we studied the transmission of THz signals in indoor scenarios. We introduced physical parameters relevant to the THz indoor channel, such as line-of-sight (LoS) path loss, power distributions, temporal and spatial properties, and associations between THz multipath properties. These parameters were integrated with the RT-SVM channel model and applied to THz indoor communication. Numerical simulations demonstrate that the proposed hybrid channel model enhances THz system performance and outperforms traditional statistical and geometric-based stochastic channel models in terms of temporal and spatial dimensions, contributing to frequency loss variations.

Power Conversion from Solar Panels using a 3000-watt Inverter

The advancement of inverter technology has seen rapid growth and widespread adoption across various industrial and commercial sectors. The output wave of the inverter has seen significant development since the introduction of the multilevel inverter. By utilizing multilevel inverters, the strain on switch components can be alleviated due to the low frequency of the power wave. This results in generating a voltage that closely resembles the desired output of the AC voltage. The study demonstrated the efficient utilization of resistive loads by the transformer, with the average sinusoid voltage source measuring 103.6%. When conducting inverter testing with a 60 Volt resource, the current input, output, voltage, and input and output efficiency measurements are obtained using a 10 Amper resource. After analyzing the data collected from the tests, it was found that the system's output voltage fell short of the anticipated 220 Volt. When a load of 60 W/220V is added, the system's output voltage is affected, resulting in an output of 740.5 Volts. When the light load is decreased to 60W/220V, the output voltage rises to 786.9 Volt with an average inverter efficiency of 77%. The input power efficiency ranges from 25.5% to 80.0% when calculated based on the inverter power capacity limit.