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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Improving fish image detection speed with hybrid VGG16 and darknet

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Multimedia Tools and Applications 2025年第12期84卷 10551-10566页

作者： Sirigineedi, Manikanta Jagan Mohan, R.N.V. Sahu, Bandita Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering SRKR Engineering College Andhra Pradesh Bhimavaram534201 India

Fish classification and object detection are crucial tasks in the fishery industry. The use of computer vision and deep learning techniques can help automate these tasks and improve the efficiency of the fishery industry. In this paper, we propose a hybrid model of VGG16 and Darknet for fish classification and object detection. The VGG16 model is a popular deep learning model for image classification, and the Darknet model is a state-of-the-art object detection model. The hybrid model combines the strengths of both models to achieve better performance in fish classification and object detection. The proposed model is trained on a dataset of fish images, which is collected from various sources. The dataset includes images of different fish species, sizes, and orientations. The model is trained to classify fish species and detect fish objects in the images. The results of the experiments show that the proposed model outperforms the individual VGG16 and Darknet models in terms of accuracy and speed. The model achieves the training time for 500 sessions with all 951 images was only 1 h 12 min, which is very fast. Additionally, the size of the model weight file is 13 MB, which is relatively small. This suggests that the model is lightweight and efficient, which is desirable in many applications. In conclusion, the proposed hybrid model of VGG16 and Darknet is an effective solution for fish classification and object detection. The model can be applied in real-world fishery scenarios to improve the efficiency and accuracy of fishery tasks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fish

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Cumulative Capacity Credit Estimation for Renewable Energy Projects

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1643-1651页

作者： Arif S.Malik Majid A.Al Umairi the Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman Oman Electricity Transmission Company MuscatOman Electrical and Computer Engineering Department Sultan Qaboos UniversityAl-Khod 123Oman

This paper presents a novel method for accurately estimating the cumulative capacity credit(CCC)of renewable energy(RE)*** data from the main interconnected system(MIS)of Oman for 2028,where a substantial increase in RE generation is anticipated,the method is introduced alongside the traditional effective load carrying capability(ELCC)*** ensure its robustness,we compare CCC results with ELCC calculations using two distinct standards of reliability criteria:loss of load hours(LOLH)at 24 hour/year and 2.4 hour/*** method consistently gives accurate results,emphasizing its exceptional accuracy,efficiency,and simplicity.A notable feature of the method is its independence from loss of load probability(LOLP)calculations and the iterative procedures associated with analytic-based reliability ***,it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE *** innovation is of particular significance to prospective independent power producers(IPPs)in the RE sector,offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data,often restricted by privacy concerns.

关键词： Effective load carrying capability renewable energy resource capacity credit generation system reliability loss of load probability

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Enhanced prediction of agricultural CO2 emission using ensemble machine learning-based imputation approach

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Neural Computing and Applications 2024年第36期36卷 22867-22887页

作者： Thendral, M. Sathya Abinaya, S. Devi, M. K. Kavitha Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai625015 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The agricultural sector contributes significantly to greenhouse gas emissions, which cause global warming and climate change. Numerous mathematical models have been developed to predict the greenhouse gas emissions from agriculture. However, the database utilized for prediction has thousands of missing values and imbalanced data records due to various factors, such as environmental disasters, sensor failure, maintenance issues, and budgetary constraints. Many researchers have either completely discarded records with missing values or have only partially addressed the issue, leading to less precise predictions. This study proposes a machine learning-based ensemble approach that uses multiple imputation by chained equations (MICE), K-nearest neighbors (KNN), and MissForest techniques to impute the missing values by reducing uncertainties and improves robustness in predictions. Furthermore, Synthetic Minority Oversampling Technique (SMOTE R) addresses data imbalance in CO2 emissions prediction by generating synthetic data to balance the target variable's distribution in the regression problem. Thereby the proposed SMOTE R and ensemble imputation approach aims to improve prediction accuracy and reliability by tackling data completeness and distribution issues simultaneously. The experiments are conducted on the GRACEnet database shows that the proposed approach outperforms the existing work in measure of R2, MAE, and RMSE metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Greenhouse gas emissions

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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Multimedia Tools and Applications 2025年第16期84卷 15715-15733页

作者： Ajil, A. Kumar, E. Saravana Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Karnataka Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Resource allocation

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Meta-Learning Online Control for Linear Dynamical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4163-4169页

作者： Muthirayan, Deepan Kalathil, Dileep Khargonekar, Pramod P. University of California Irvine Department of Electrical Engineering and Computer Sciences IrvineCA United States Texas A&M University Department of Electrical and Computer Engineering United States

In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for a finite horizon of T time steps. The cost function and system noise at each time step are adversarial and unknown to the controller before taking the control action. Meta-learning is a broad approach where the goal is to prescribe an online policy for any new unseen task exploiting the information from other tasks and the similarity between the tasks. We propose a meta-learning online control algorithm for the control setting and characterize its performance by meta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor D/D∗ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where D is a problem constant and D∗ is a scalar that decreases with increase in the similarity between tasks. Thus, when the sequence of tasks are similar the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present experiment results to demonstrate the superior performance achieved by our meta-learning algorithm. © 1963-2012 IEEE.

关键词： Cost functions

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Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

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Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Electromagnetic Interference on Unmanned Aerial Vehicles (UAVs): A Case Study of High Power Transmission Line Impacts

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7501-7513页

作者： Salehi Rad, Shervin Zheng, Zilong Kheirollahi, Reza Mostafa, Amr Zhao, Shuyan Wang, Yao Chevinly, Javad Nadi, Elias Bensala, Tarik Zhang, Hua Friedman, Gary Lu, Fei Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA19104 United States Lehigh University Department of Electrical and Computer Engineering BethlehemPA18015 United States

Nowadays, unmanned aerial vehicles (UAVs) play a significant role in transmission line inspection. This article focuses on the inertial measurement unit (IMU) as a critical component to study the impacts of electromagnetic interference. A case study is conducted by selecting 500-kV/25-kA 60-Hz transmission lines as sources of strong electromagnetic fields and investigates their influences on an IMU unit and operation of an UAV. An experimental testing methodology is proposed to explore the impacts of magnetic and electric fields separately. A high-power inverter-based testing platform is implemented to excite a 2.2-kA planar coil and a 7.5-kA solenoid coil to generate magnetic fields and emulate high current impacts from transmission lines. Meanwhile, a pair of metal plates are adopted to generate a 2-kV/m electric field and study the high-voltage impacts. Multiple long-time continuous testings of the DJI Marvic 2 drone have been conducted in the generated magnetic and electric fields. It validates that the onboard magnetometer is sensitive to magnetic fields, while other sensors remain immune to both magnetic and electric fields, at least up to 3100 μT and 2 kV/m, respectively. © 2015 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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