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  • Accuracy of marine gravimetric measurements in terms of geodetic coordinates of land reference benchmark
    • Krzysztof Pyrchla
    • Kamil Łapiński
    • Jakub Szulwic
    • Wojciech Jurczak
    • Marek Przyborski
    • Jerzy Pyrchla
    2024 Pełny tekst Eksploatacja i Niezawodność - Maintenance and Reliability

    The article presents how the values of (3D) coordinates of land reference points affect the results of gravimetric measurements made from the ship in sea areas. These measurements are the basis for 3D maritime inertial navigation, improving ships' operational safety. The campaign verifying the network absolute point coordinates used as a reference point for relative marine gravity measurements was described. The obtained values were compared with catalogue values. In verification of network points 3D position the satellite data Global Satellite Navigation System (GNSS) and ground supporting systems (GBAS) was used. In this example, the height difference of the land reference point was 0.32 m. As a consequence, the offset budget of the marine campaign was affected in the range of up to 0.35 mGal. The influence on gravity free-air anomaly was not constant over the entire area covered by the campaign.

  • Accurate Post-processing of Spatially-Separated Antenna Measurements Realized in Non-Anechoic Environments
    • Adrian Bekasiewicz
    • Vorya Waladi
    • Tom Dhaene
    • Bartosz Czaplewski
    2024

    Antenna far-field performance is normally evaluated in expensive laboratories that maintain strict control over the propagation environment. Alternatively, the responses can be measured in non-anechoic conditions and then refined to extract the information on the structure field-related behavior. Here, a framework for correction of antenna measurements performed in non-anechoic test site has been proposed. The method involves automatic synchronization (in time-domain) of spatially separated measurements followed by their combination so as to augment the fraction of the signal that represents the antenna performance while suppressing the interferences. The method has been demonstrated based on six experiments performed in an office room. The performance improvement due to proposed post-processing amounts to 9.4 dB, which is represents up to over 5 dB improvement compared to the state-of-the-art methods.

  • Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy
    • Changqi Luo
    • Shun-Peng Zhu
    • Behrooz Keshtegar
    • Wojciech Macek
    • Ricardo Branco
    • Debiao Meng
    2024 COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

    Efficient structural reliability analysis method is crucial to solving reliability analysis of complex structural problems. High-computational cost and low-failure probability problems greatly limit the efficiency in structural reliability analysis problems, causing the safety and reliability of the structure to be questioned. In this work, a highly efficient structural reliability analysis method coupling active Kriging algorithm with conjugate first order reliability method (AK-CFORM) is proposed. Specifically, the resample strategy is considered to reduce the number of samples evaluated in each active learning process; the uniform sampling is used to better balance global and local optimal problems; the conjugate map is used to improve the robustness of analytical first order reliability method; and the approximate numerical differential formula is proposed to solve the problems of non-convergence when solving the gradient of the Kriging surrogate model. Finally, three numerical cases and four engineering cases are used to illustrate the effectiveness and robustness of the proposed method. The results show that the proposed AK-CFORM has greater advantages in the number of calling system response and surrogate model with robust and accurate performance.

  • Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
    • Neda Asgarkhani
    • Farzin Kazemi
    • Robert Jankowski
    2024

    This research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story steel structures considering four soil type effects. To prepare the dataset, 3584 incremental dynamic analysis (IDA) were performed on 64 structures. The research employs 6-, and 8-story structures to validate the AL-Ensemble ML model's effectiveness, showing it achieves the highest accuracy among conventional ML models, with an R2 of 98.4%. Specifically, it accurately predicts the RID of floor levels in a 6-story structure with an accuracy exceeding 96.6%. Additionally, the programming code identifies the specific damaged floor level in a building, facilitating targeted local retrofitting instead of retrofitting the entire structure promising a reduction in retrofitting costs while enhancing prediction accuracy.

  • Activity-based payments: alternative (anonymous) online payment model
    • Rafał Leszczyna
    2024 International Journal of Information Security

    Electronic payments are the cornerstone of web-based commerce. A steady decrease in cash usage has been observed, while various digital payment technologies are taking over. They process sensitive personal information raising concerns about its potentially illicit usage. Several payment models that confront this challenge have been proposed. They offer varying levels of anonymity and readiness for adoption. The aim of this study was to broaden the portfolio with a solution that assures the highest level of anonymity and is well applicable. An empirical design research study with prototyping and conceptual research with a proposed construct were employed for this purpose. As a result, the Activity-Based Payment (ABP) model was proposed. It introduces a different mode of completing a payment transaction based on performing specific activities on a web location indicated by the payee. The anonymity properties of the solution, as well as its performance and applicability have been evaluated showing its particular suitability to micropayment and small payment scenarios.

  • Actual and reference evapotranspiration for a natural, temperate zone fen wetland – Upper Biebrza case study
    • Malgorzata Kleniewska
    • Tomasz Berezowski
    • Dorota Mitrowska
    • Sylwia Szporak-Wasilewska
    • Wojciech Ciezkowski
    2024 Pełny tekst Journal of Water and Land Development

    Evapotranspiration is the key and predominant component of the water balance in wetlands. Direct evapotranspiration measurements are challenging in wetlands due to their remoteness and high surface water level. This article describes the actual (ETa and reference evapotranspiration (ET0) from a cultivated wet meadow located in the Biebrza National Park – the largest national park in north-east Poland, Central Europe. The data were sourced from a micrometeorological station equipped with an eddy covariance system to measure heat and vapour fluxes and such meteorological elements as radiation balance components, air temperature and humidity. The values of directly measured ETa were presented daily in the context of available energy and ET0. Daily sums of ETa ranged from below 0.2 mm in winter to 6.5 mm in summer. The share of daily sums of ETa in the ET0 usually ranged from 50 to 60%, with extreme values from 10 to 170%. Aside from giving more insight into Biebrza wetlands’ functioning, the actual data produced in this study may be used instead of indirect methods, which were used the most in modelling wetlands areas.

  • Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
    • Filip Szatkowski
    • Mateusz Pyła
    • Marcin Przewięźlikowski
    • Sebastian Cygert
    • Bartłomiej Twardowski
    • Tomasz Trzciński
    2024

    In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with outof-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL models. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main models during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks. The source code for our method is available at https://github.com/fszatkowski/cl-teacher-adaptation.

  • Adaptacyjny system oświetlania dróg oraz inteligentnych miast
    • Tomasz Śmiałkowski
    2024 Pełny tekst

    Przedmiotem rozprawy jest zbadanie praktycznej możliwości wykrywania w czasie rzeczywistym anomalii w systemie oświetlenia drogowego w oparciu o analizę danych ze inteligentnych liczników energii. Zastosowanie inteligentnych liczników energii elektrycznej (Smart Meter) w systemach oświetlenia drogowego stwarza nowe możliwości w zakresie automatycznej diagnostyki takich niepożądanych zjawisk jak awarie lamp, odstępstwa od harmonogramu czy tez kradzieże energii z sieci zasilającej. Rozwiązanie takie wpisuje się w koncepcję inteligentnych miast (Smart City) gdzie zastosowanie adaptacyjnego systemu oświetlenia stwarza nowe wyzwania dla funkcji monitorowania. Zbadano metody uczenia maszynowego oparte o modele regresyjne oraz rekurencyjne sieci neuronowe. Zaproponowano praktyczne podejście oparte na zastosowaniu algorytmów czasu rzeczywistego, nienadzorowanych oraz używających ograniczonych zasobów obliczeniowych możliwych do implementacji w urządzeniach przemysłowych. Algorytmy przetestowano na rzeczywistych danych pochodzących z instalacji systemu oświetlenia i wykazano, że obie metody umożliwiają stworzenie samouczących algorytmów detekcji anomalii, działających w czasie rzeczywistym i że jest możliwa ich implementacja na urządzeniach warstwy Edge Computing. W rozprawie przedstawiono również architekturę uniwersalnej platformy sterowania elementami infrastruktury oświetleniowej opracowanej przy udziale autora, jako głównego konstruktora, w ramach projektu rozwojowego „INFOLIGHT - Chmurowa platforma oświetleniowa dla inteligentnych miast”.

  • Adaptacyjny system sterowania ruchem drogowym
    • Andrzej Sroczyński
    2024 Pełny tekst

    Adaptacyjny system sterowania ruchem drogowym to rodzaj systemu sterowania, który dynamicznie, w czasie rzeczywistym, dostosowuje swoje parametry w oparciu o bieżące warunki ruchu drogowego. Celem niniejszej rozprawy jest sprawdzenie wpływu wybranych cech systemu, zbudowanego w oparciu o zaprojektowane i zbudowane z udziałem autora inteligentne znaki drogowe, na wybrane parametry mające wpływ na bezpieczeństwo i płynność ruchu. W pierwszej kolejności zbadany został, na podstawie eksperymentu symulacyjnego, wpływ metody stopniowania redukcji prędkości na płynność ruchu. Drugim przedmiotem badań był wpływ odległości pomiędzy kolejnymi znakami ograniczenia prędkości na wariancję prędkości pojazdów. Ostatnim badanym aspektem była weryfikacja możliwości testowania modeli uczenia maszynowego, wytrenowanych na danych rzeczywistych, za pomocą danych syntetycznych, uzyskanych w drodze symulacji. Wyniki badań posłużyły do udowodnienia trzech tez badawczych, sformułowanych w niniejszej pracy. Praca zawiera ponadto rozdział w całości poświęcony opisowi praktycznej realizacji demonstratora adaptacyjnego systemu sterowania ruchem drogowym. Przedstawione zostały instalacje eksperymentalne oraz niektóre rezultaty badań terenowych, zaś dodatkiem do rozprawy jest przegląd konstrukcji opracowanych demonstratorów inteligentnych znaków drogowych.

  • Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation
    • Jan Cychnerski
    • Maciej Zakrzewski
    • Dominik Kwiatkowski
    2024

    In computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor Segmentation Benchmark show that the learned window parameters often converge to a range encompassing clinically used windows but wider, suggesting that adjacent data may contain useful information for machine learning. This layer may enhance model efficiency with just 2 additional parameters.

  • Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
    • Taeho Jeong
    • Leifur Leifsson
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024

    In this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using the three-dimensional Hartmann function and evaluating both full and partial sets of HPs, adaptive HPOs are compared to traditional static HPO (HPO-static) that keep HPs constant. The results reveal that adaptive HPO strategies outperform HPOstatic, and the frequency of tuning and number of tuning HPs impact both the optimization accuracy and computational efficiency. Specifically, adaptive HPOs demonstrate rapid convergence rates (HPO-1itr at 28 iterations, HPO-5itr at 26 for full HPs; HPO-1itr at 13, HPO-5itr at 28 iterations for selected HPs), while HPO-static fails to approximate the minimum within the allocated 45 iterations for both scenarios. Mainly, HPO-5itr is the most balanced approach, found to require 21% of the time taken by HPO-1itr for tuning full HPs and 29% for tuning a subset of HPs. This work demonstrates the importance of adaptive HPO and sets the stage for future research.

  • Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
    • Zhongyang Wang
    • Youqing Wang
    • Zdzisław Kowalczuk
    2024 IEEE-CAA Journal of Automatica Sinica

    In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming (ADP) technique based on the internal model principle (IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback, merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that in this concept you do not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

  • Adaptive Sampling for Non-intrusive Reduced Order Models Using Multi-Task Variance
    • Abhijnan Dikshit
    • Leifur Leifsson
    • Sławomir Kozieł
    • Anna Pietrenko-Dąbrowska
    2024

    Non-intrusive reduced order modeling methods (ROMs) have become increasingly popular for science and engineering applications such as predicting the field-based solutions for aerodynamic flows. A large sample size is, however, required to train the models for global accuracy. In this paper, a novel adaptive sampling strategy is introduced for these models that uses field-based uncertainty as a sampling metric. The strategy uses Monte Carlo simulations to propagate the uncertainty in the prediction of the latent space of the ROM obtained using a multitask Gaussian process to the high-dimensional solution of the ROM. The high-dimensional uncertainty is used to discover new sampling locations to improve the global accuracy of the ROM with fewer samples. The performance of the proposed method is demonstrated on the environment model function and compared to one-shot sampling strategies. The results indicate that the proposed adaptive sampling strategies can reduce the mean relative error of the ROM to the order of 8 × 10−4 which is a 20% and 27% improvement over the Latin hypercube and Halton sequence sampling strategies, respectively at the same number of samples.

  • Addressing challenges of BiVO4 light-harvesting ability through vanadium precursor engineering and sub-nanoclusters deposition for peroxymonosulfate-assisted photocatalytic pharmaceuticals removal
    • Marta Kowalkińska
    • Alexey Maximenko
    • Aleksandra Szkudlarek
    • Karol Sikora
    • Anna Zielińska-Jurek
    2024 Pełny tekst SEPARATION AND PURIFICATION TECHNOLOGY

    In this study, we present a complex approach for increasing light utilisation and peroxymonosulfate (PMS) activation in BiVO4-based photocatalyst. This involves two key considerations: the design of the precursor for BiVO4 synthesis and interface engineering through CuOx sub-nanoclusters deposition. The designed precursor of ammonium methavanadate (NH4VO3, NHV) leads to reduction in particle size, better dispersion and improved light harvesting ability, confirmed by the calculations of the local volume rate of photon absorption (LVRPA) using the Six-Flux Radiation Absorption-Scattering model. The morphological changes result in a significant improvement in photocatalytic activity under visible light for the degradation of pharmaceuticals (naproxen and ofloxacin) compared to the commercial NH4VO3. Additionally, CuOx sub-nanoclusters were deposited on designed BiVO4 and characterised using X-ray absorption near edge structure (XANES). The presence of subnanoclusters enhanced charge carriers separation, resulting in an increase in the apparent rate constants of 1.60 and 3.32-times for photocatalytic NPX and OFL removal, respectively. The application of obtained Vis light active photocatalysts in the presence of 0.1 mM PMS resulted in remarkably more efficient degradation of NPX (100 % within 60 min) and OFL (98.2 % within 120 min). PMS/Vis420/CuOx/BiVO4 system exhibited high stability and reusability in the subsequent cycles of photodegradation. However, high PMS dosage induced Bi leaching which may cause the instability of the photocatalyst. Finally, to address the environmental implications of pharmaceutical removal and adhere to the Guidelines for drinking-water quality, toxicity assessments using Vibrio fischeri bacteria were performed and compared to a quantitative structure–activity relationship (QSAR) model.

  • Addressing the Weaknesses of Multi-Criteria Decision-Making Methods using Python
    • Semra Erpolat Tasabat
    • Tugba KIRAL Ozkan
    • Olgun Aydin
    2024

    The book aims to draw attention to the weaknesses in Multi-Criteria Decision-Making (MCDM) methods and provide insights to improve the decision-making process. By addressing these weaknesses, it seeks to enhance the accuracy and effectiveness of MCDM methods in selecting the best alternatives in various fields. The book covers popular MCDM methods such as TOPSIS, ELECTRE, VIKOR, and PROMETHEE. It compares traditional methods with the proposed modified Human Development Index (HDI) data using Python code examples. The target audience for the book includes computer scientists, engineers, business, and financial management professionals, as well as anyone interested in MCDM and its applications.

  • Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
    • Krzysztof Laddach
    • Rafał Łangowski
    2024 APPLIED SOFT COMPUTING

    A problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included an extended verification study for the classical XOR classification problem. In addition, the developed learning method has been used to provide a spiking neural black-box model of fast processes occurring in a pressurised water nuclear reactor. The obtained simulation results demonstrate satisfactory effectiveness of the proposed approach.

  • Adoption of the F-statistic of Fisher-Snedecor distribution to analyze importance of impact of modifications of injector opening pressure of a compression ignition engine on specific enthalpy value of exhaust gas flow
    • Patrycja Puzdrowska
    2024 Pełny tekst Combustion Engines

    This article analyzes the effect of modifications of injector opening pressure on the operating values of a compression ignition engine, including the temperature of the fumes. A program of experimental investigation is described, considering the available test stand and measurement capabilities. The structure of the test stand on which the experimental measurements were conducted is presented. The method of introducing real modifications of injector opening pressure to the existing test engine was characterized. It was proposed to use F statistic of Fisher-Snedecor (F-S) distribution to evaluate the importance of the impact of modifications of injector opening pressure on the specific enthalpy of the flue gas flow. Qualitative and statistical studies of the results achieved from the measurements were carried out. The specific enthalpy of the fumes for a single cycle of the compression ignition engine, determined from the course of rapidly variable flue gas temperature, was analyzed. The results of these studies are presented and the usable adoption of this type of assessment in parametric diagnosing of compression ignition engines is discussed.

  • Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship
    • Cunlong Fan
    • Victor Bolbot
    • Jakub Montewka
    • Di Zhang
    2024 ACCIDENT ANALYSIS AND PREVENTION

    The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

  • Advanced nanomaterials and metal-organic frameworks for catalytic bio-diesel production from microalgal lipids – A review
    • Zohaib Saddique
    • Muhammad Imran
    • Shoomaila Latif
    • Ayesha Javaid
    • Shahid Nawaz
    • Nemira Zilinskaite
    • Marcelo Franco
    • Ausra Baradoke
    • Ewa Wojciechowska
    • Grzegorz Boczkaj
    2024 JOURNAL OF ENVIRONMENTAL MANAGEMENT

    Increasing energy demands require exploring renewable, eco-friendly (green), and cost-effective energy resources. Among various sources of biodiesel, microalgal lipids are an excellent resource, owing to their high abundance in microalgal biomass. Transesterification catalyzed by advanced materials, especially nanomaterials and metal-organic frameworks (MOFs), is a revolutionary process for overcoming the energy crisis. This review elaborates on the conversion of microalgal lipids (including genetically modified algae) into biodiesel while primarily focusing on the transesterification of lipids into biodiesel by employing catalysts based on above mentioned advanced materials. Furthermore, current challenges faced by this process for industrial scale upgradation are presented with future perspectives and concluding remarks. These materials offer higher conversion (>90%) of microalgae into biodiesel. Nanocatalytic processes, lack the need for higher pressure and temperature, which simplifies the overall process for industrial-scale application. Green biodiesel production from microalgae offers better fuel than fossil fuels in terms of performance, quality, and less environmental harm. The chemical and thermal stability of advanced materials (particularly MOFs) is the main benefit of the blue recycling of catalysts. Advanced materials-based catalysts are reported to reduce the risk of biodiesel contamination. While purity of glycerin as side product makes it useful skin-related product. However, these aspects should still be controlled in future studies. Further studies should relate to additional aspects of green production, including waste management strategies and quality control of obtained products. Finally, catalysts stability and recycling aspects should be explored.

  • Advanced seismic control strategies for smart base isolation buildings utilizing active tendon and MR dampers
    • Morteza Akbari
    • Javad Palizvan Zand
    • Tomasz Falborski
    • Robert Jankowski
    2024 ENGINEERING STRUCTURES

    This paper investigates the seismic behaviour of a five-storey shear building that incorporates a base isolation system. Initially, the study considers passive base isolation and employs a multi-objective archived-based whale optimization algorithm called MAWOA to optimize the parameters of base isolation. Subsequently, a novel model is proposed, which incorporates an interval type-2 Takagi-Sugeno fuzzy logic controller (IT2TSFLC) utilizing clustering techniques. The building includes Magneto-rheological (MR) dampers installed at the base isolation level and two active tendons positioned on the first and second storeys of the structure. The semi-active control force of the base isolation with MR dampers is determined by the fuzzy system, while the active control force for the active tendons is computed using the linear quadratic regulator (LQR) algorithm, enabling control force provision during seismic events. The primary objective of this model is to enhance the seismic control of the building. Therefore, it is classified as a proposed model. The structural system is subjected to seismic analyses, considering three different structural configurations: uncontrolled, equipped with passive base isolation, and equipped with semi-active base isolation combining MR dampers and active tendons. The findings of the research demonstrate that by considering the optimization of parameters of the passive base isolation based on the white noise scenario and using these parameters as design parameters, during seismic analysis of the structure in some earthquakes, increased structural responses were observed when compared to uncontrolled structure, highlighting a potential risk. Nevertheless, the proposed system effectively addresses this drawback of passive control systems by markedly reducing structural responses, as compared to both passive base isolation and uncontrolled structure. These results suggest that the proposed system is an effective solution for mitigating seismic risks in structural seismic control.