bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Thermal Analysis Method Based on a Multi-Physics Two-Way Coupled Method and Its Application to Submersible Permanent Magnet Synchronous Motors
Authors: Xiao Wensheng , Tan Liping , Cui Junguo , Wang Hongyan , Li Changjiang , Qin Haozhi , Zhang Jun
Published: March 2023

As a submersible permanent magnet synchronous motor (SPMSM) must function in downhole high temperature crude oil for long periods of time, the accurate prediction of its temperature rise is crucial to improve the stability of the motor. However, the analysis of its temperature field involves multiple physical fields, such as the electric field, the magnetic field, the fluid field and so on, and it is difficult to calculate accurately. Motor loss is usually loaded as a fixed value when calculating the temperature field, while in reality, the loss always changes with temperature. Therefore, the calculation of temperature is inaccurate using this one-way coupled method. In this paper, the relationship between loss and temperature is investigated and an electromagnetic-thermal fluid multi-physics two-way coupled analysis method suitable for SPMSM is proposed. The loss can be loaded as a variable by this new two-way coupled method, which significantly improves the temperature calculation accuracy. In order to prove the feasibility of the two-way coupled method, experiments based of a prototype of high torque and low speed SPMSM and cavity pump are conducted with the fiber Bragg grating (FBG) sensor measuring SPMSM temperature. Finally, the multi-physics two-way coupled method proposed in this paper is proven to be rational and feasible in the analysis of the SPMSM temperature rise.
DBoTPM: A Deep Neural Network-Based Botnet Prediction Model
Authors: Haq Mohd Anul
Published: March 2023

Internet of things (IoT) devices’ evolution and growth have boosted system efficiency, reduced human labour, and improved operational efficiency; however, IoT devices pose substantial security and privacy risks, making them highly vulnerable to botnet attacks. Botnet attacks are capable of degrading the performance of an IoT system in a way that makes it difficult for IoT network users to identify them. Earlier studies mainly focused on the detection of IoT botnets, and there was a gap in predicting the botnet attack due to their complex behaviour, repetitive nature, uncertainty, and almost invisible presence in the compromised system. Based on the gaps, it is highly required to develop efficient and stable AI models that can reliably predict botnet attacks. The current study developed and implemented DBoTPM, a novel deep-neural-network-based model for botnet prediction. The DBoTPM was optimized for performance and less computational overhead by utilizing rigorous hyperparameter tuning. The consequences of overfitting and underfitting were mitigated through dropouts. The evaluation of the DBoTPM demonstrated that it is one of the most accurate and efficient models for botnet prediction. This investigation is unique in that it makes use of two real datasets to detect and predict botnet attacks with efficient performance and faster response. The results achieved through the DBoTPM model were assessed against prior research and found to be highly effective at predicting botnet attacks with a real dataset.
VEDAM: Urban Vegetation Extraction Based on Deep Attention Model from High-Resolution Satellite Images
Authors: Yang Bin , Zhao Mengci , Xing Ying , Zeng Fuping , Sun Zhaoyang
Published: March 2023

With the rapid development of satellite and internet of things (IoT) technology, it becomes more and more convenient to acquire high-resolution satellite images from the ground. Extraction of urban vegetation from high-resolution satellite images can provide valuable suggestions for the decision-making of urban management. At present, deep-learning semantic segmentation has become an important method for vegetation extraction. However, due to the poor representation of context and spatial information, the effect of segmentation is not accurate. Thus, vegetation extraction based on Deep Attention Model (VEDAM) is proposed to enhance the context and spatial information representation ability in the scenario of vegetation extraction from satellite images. Specifically, continuous convolutions are used for feature extraction, and atrous convolutions are introduced to obtain more multi-scale context information. Then the extracted features are enhanced by the Spatial Attention Module (SAM) and the atrous spatial pyramid convolution functions. In addition, image-level feature obtained by image pooling encoding global context further improves the overall performance. Experiments are conducted on real datasets Gaofen Image Dataset (GID). From the comparative experimental results, it is concluded that VEDAM achieves the best mIoU (mIoU = 0.9136) of vegetation semantic segmentation.
Pixel-Level Fusion Approach with Vision Transformer for Early Detection of Alzheimer’s Disease
Authors: Odusami Modupe , Maskeliūnas Rytis , Damaševičius Robertas
Published: March 2023

Alzheimer’s disease (AD) has become a serious hazard to human health in recent years, and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging input can help identify AD in the early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI) stages from normal cognitive development using magnetic resonance imaging (MRI) and positron emission tomography (PET). MRI provides useful information on brain structural abnormalities, while PET data provide the difference between physiological and pathological changes in brain anatomy. The precision of diagnosing AD can increase when these data are combined. However, they are heterogeneous and appropriate, and an adequate number of features are required for AD classification. This paper proposed a multimodal fusion-based approach that uses a mathematical technique called discrete wavelet transform (DWT) to analyse the data, and the optimisation of this technique is achieved through transfer learning using a pre-trained neural network called VGG16. The final fused image is reconstructed using inverse discrete wavelet transform (IDWT). The fused images are classified using a pre-trained vision transformer. The evaluation of the benchmark Alzheimer’s disease neuroimaging initiative (ADNI) dataset shows an accuracy of 81.25% for AD/EMCI and AD/LMCI in MRI test data, as well as 93.75% for AD/EMCI and AD/LMCI in PET test data. The proposed model performed better than existing studies when tested on PET data with an accuracy of 93.75%.
An Encryption Application and FPGA Realization of a Fractional Memristive Chaotic System
Authors: Mohamed Sara M. , Sayed Wafaa S. , Madian Ahmed H. , Radwan Ahmed G. , Said Lobna A.
Published: March 2023

The work in this paper extends a memristive chaotic system with transcendental nonlinearities to the fractional-order domain. The extended system’s chaotic properties were validated through bifurcation analysis and spectral entropy. The presented system was employed in the substitution stage of an image encryption algorithm, including a generalized Arnold map for the permutation. The encryption scheme demonstrated its efficiency through statistical tests, key sensitivity analysis and resistance to brute force and differential attacks. The fractional-order memristive system includes a reconfigurable coordinate rotation digital computer (CORDIC) and Grünwald–Letnikov (GL) architectures, which are essential for trigonometric and hyperbolic functions and fractional-order operator implementations, respectively. The proposed system was implemented on the Artix-7 FPGA board, achieving a throughput of 0.396 Gbit/s.
Secure Reversible Data Hiding Using Block-Wise Histogram Shifting
Authors: Kamil Khudhair Samar , Sahu Monalisa , K. R. Raghunandan , Sahu Aditya
Published: March 2023

Reversible data hiding (RDH) techniques recover the original cover image after data extraction. Thus, they have gained popularity in e-healthcare, law forensics, and military applications. However, histogram shifting using a reversible data embedding technique suffers from low embedding capacity and high variability. This work proposes a technique in which the distribution obtained from the cover image determines the pixels that attain a peak or zero distribution. Afterward, adjacent histogram bins of the peak point are shifted, and data embedding is performed using the least significant bit (LSB) technique in the peak pixels. Furthermore, the robustness and embedding capacity are improved using the proposed dynamic block-wise reversible embedding strategy. Besides, the secret data are encrypted before embedding to further strengthen security. The experimental evaluation suggests that the proposed work attains superior stego images with a peak signal-to-noise ratio (PSNR) of more than 58 dB for 0.9 bits per pixel (BPP). Additionally, the results of the two-sample t-test and the Kolmogorov–Smirnov test reveal that the proposed work is resistant to attacks.
Hybrid Graph Neural Network Recommendation Based on Multi-Behavior Interaction and Time Sequence Awareness
Authors: Jia Mingyu , Liu Fang’ai , Li Xinmeng , Zhuang Xuqiang
Published: March 2023

In recent years, mining user multi-behavior information for prediction has become a hot topic in recommendation systems. Usually, researchers only use graph networks to capture the relationship between multiple types of user-interaction information and target items, while ignoring the order of interactions. This makes multi-behavior information underutilized. In response to the above problem, we propose a new hybrid graph network recommendation model called the User Multi-Behavior Graph Network (UMBGN). The model uses a joint learning mechanism to integrate user–item multi-behavior interaction sequences. We designed a user multi-behavior information-aware layer to focus on the long-term multi-behavior features of users and learn temporally ordered user–item interaction information through BiGRU units and AUGRU units. Furthermore, we also defined the propagation weights between the user–item interaction graph and the item–item relationship graph according to user behavior preferences to capture more valuable dependencies. Extensive experiments on three public datasets, namely MovieLens, Yelp2018, and Online Mall, show that our model outperforms the best baselines by 2.04%, 3.82%, and 3.23%.
A 1 + α Order Generalized Butterworth Filter Structure and Its Field Programmable Analog Array Implementation
Authors: Nako Julia , Psychalinos Costas , Elwakil Ahmed S.
Published: March 2023

Fractional-order Butterworth filters of order 1 + α (0 < α < 1) can be implemented by a unified structure, using the method presented in this paper. The main offered benefit is that the cutoff frequencies of the filters are fully controllable using a very simple method and, also, various types of filters (e.g., low-pass, high-pass, band-pass, and band-stop) could be realized. Thanks to the employment of a Field Programmable Analog Array device, the implementation of the introduced method is fully reconfigurable, in the sense that various types of filter functions as well as their order are both programmable.
Attention-Oriented Deep Multi-Task Hash Learning
Authors: Wang Letian , Meng Ziyu , Dong Fei , Yang Xiao , Xi Xiaoming , Nie Xiushan
Published: March 2023

Hashing has wide applications in image retrieval at large scales due to being an efficient approach to approximate nearest neighbor calculation. It can squeeze complex high-dimensional arrays via binarization while maintaining the semantic properties of the original samples. Currently, most existing hashing methods always predetermine the stable length of hash code before training the model. It is inevitable for these methods to increase the computing time, as the code length converts, caused by the task requirements changing. A single hash code fails to reflect the semantic relevance. Toward solving these issues, we put forward an attention-oriented deep multi-task hash learning (ADMTH) method, in which multiple hash codes of varying length can be simultaneously learned. Compared with the existing methods, ADMTH is one of the first attempts to apply multi-task learning theory to the deep hashing framework to generate and explore multi-length hash codes. Meanwhile, it embeds the attention mechanism in the backbone network to further extract discriminative information. We utilize two common available large-scale datasets, proving its effectiveness. The proposed method substantially improves retrieval efficiency and assures the image characterizing quality.
Analysis of a Personalized Provision of Service Level Agreement (SLA) Algorithm
Authors: Peoples Cathryn , Tariq Zeeshan , Georgalas Nektarios , Moore Adrian
Published: March 2023

The existence of restricted Service Level Agreement (SLA) choices, which typically correspond with a couple of service tiers, can result in a customer accepting a service that may not effectively respond to their needs. From a service provider perspective, it is also a less than optimum business model, with capacity being reserved for customers who will not use it and subsequently being unavailable for customers who would. We, therefore, advocate the use of personalized SLAs to avoid such situations, which can ideally be set up without the assistance of a human operator. We suggest classifying customers according to their distinguishing features, one of which includes a customer’s propensity to have online devices in their home. Through the results presented in this paper, we are confident about the accuracy of our classification results; however, we recognize that there are opportunities for latency improvements in the efficiency of the process.
IoT Data Sharing Platform in Web 3.0 Using Blockchain Technology
Authors: Razzaq Abdul , Altamimi Ahmed B. , Alreshidi Abdulrahman , Ghayyur Shahbaz Ahmed Khan , Khan Wilayat , Alsaffar Mohammad
Published: March 2023

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing and sharing rely on third-party sources of information (TTPs) that gather data from one party, then transmit it to the other. As a result of TTPs’ involvement, such IoT systems suffer from many issues including but not limited to security, transparency, trust, and immutability as a result of the involvement of the company. Moreover, a multitude of technical impediments, such as the computation and storage poverty of IoTs, privacy concerns, and energy efficiency, enhances the challenges for IoTs. To address these issues of IoT security, we propose a blockchain-enabled open IoT data-sharing framework based on the potential of the interplanetary file system (IPFS). We have used a case study-based approach to evaluate the proposed solution. It is submitted that the proposed scenario is implemented by building smart contracts in Solidity and deploying them on the local Ethereum test network, using the Solidity programming language. With the implementation of smart contracts on the blockchain for access roles in IoT data sensing, the proposed solution advocates for a blockchain-based approach to data security for IoT systems that makes use of smart contracts for access roles.
The BciAi4SLA Project: Towards a User-Centered BCI
Authors: Gena Cristina , Hilviu Dize , Chiarion Giovanni , Roatta Silvestro , Bosco Francesca M. , Calvo Andrea , Mattutino Claudio , Vincenzi Stefano
Published: March 2023

The brain–computer interfaces (BCI) are interfaces that put the user in communication with an electronic device based on signals originating from the brain. In this paper, we describe a proof of concept that took place within the context of BciAi4Sla, a multidisciplinary project involving computer scientists, physiologists, biomedical engineers, neurologists, and psychologists with the aim of designing and developing a BCI system following a user-centered approach, involving domain experts and users since initial prototyping steps in a design–test–redesign development cycle. The project intends to develop a software platform able to restore a communication channel in patients who have compromised their communication possibilities due to illness or accidents. The most common case is the patients with amyotrophic lateral sclerosis (ALS). In this paper, we describe the background and the main development steps of the project, also reporting some initial and promising user evaluation results, including real-time performance classification and a proof-of-concept prototype.
A New Social Media-Driven Cyber Threat Intelligence
Authors: Sufi Fahim
Published: March 2023

Cyber threats are projected to cause USD 10.5 trillion in damage to the global economy in 2025. Comprehending the level of threat is core to adjusting cyber posture at the personal, organizational, and national levels. However, representing the threat level with a single score is a daunting task if the scores are generated from big and complex data sources such as social media. This paper harnesses the modern technological advancements in artificial intelligence (AI) and natural language processing (NLP) to comprehend the contextual information of social media posts related to cyber-attacks and electronic warfare. Then, using keyword-based index generation techniques, a single index is generated at the country level. Utilizing a convolutional neural network (CNN), the innovative process automatically detects any anomalies within the countrywide threat index and explains the root causes. The entire process was validated with live Twitter feeds from 14 October 2022 to 27 December 2022. During these 75 days, AI-based language detection, translation, and sentiment analysis comprehended 15,983 tweets in 47 different languages (while most of the existing works only work in one language). Finally, 75 daily cyber threat indexes with anomalies were generated for China, Australia, Russia, Ukraine, Iran, and India. Using this intelligence, strategic decision makers can adjust their cyber preparedness for mitigating the detrimental damages afflicted by cyber criminals.
High-Performance Embedded System for Offline Signature Verification Problem Using Machine Learning
Authors: Tariq Umair , Hu Zonghai , Tariq Rokham , Iqbal Muhammad Shahid , Sadiq Muhammad
Published: March 2023

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%.
Research on Improved Wavelet Threshold Denoising Method for Non-Contact Force and Magnetic Signals
Authors: Li Xiaoxiao , Liao Kexi , He Guoxi , Zhao Jianhua
Published: March 2023

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.
Energy Efficient Data Dissemination for Large-Scale Smart Farming Using Reinforcement Learning
Authors: Ali Muhammad Yasir , Alsaeedi Abdullah , Shah Syed Atif Ali , Yafooz Wael M. S. , Malik Asad Waqar
Published: March 2023

Smart farming is essential to increasing crop production, and there is a need to consider the technological advancements of this era; modern technology has helped us to gain more accuracy in fertilizing, watering, and adding pesticides to the crops, as well as monitoring the conditions of the environment. Nowadays, more and more sophisticated sensors are being developed, but on a larger scale, agricultural networks and the efficient management of them is very crucial in order to obtain proper benefits from technology. Our idea is to achieve sustainability in large-scale farms by improving communication between wireless sensor nodes and base stations. We want to increase communication efficiency by introducing machine learning algorithms. Reinforcement learning is the area of machine learning which is concerned with how involved agents are supposed to take action in specified environments to maximize reward and achieve a common goal. In our network, a large number of sensors are being deployed on large-scale fields; reinforcement learning is used to find the optimal set of paths towards the base station. After a number of successful paths have been developed, they are then used to transmit the sensed data from the fields. The simulation results have shown that in larger scales, our proposed model had less transmission delay than the shortest path transmission model and broadcasting techniques that were tested against the data transmission paths developed by reinforcement learning.
RESTful API Analysis, Recommendation, and Client Code Retrieval
Authors: Ma Shang-Pin , Hsu Ming-Jen , Chen Hsiao-Jung , Lin Chuan-Jie
Published: March 2023

Numerous companies create innovative software systems using Web APIs (Application Programming Interfaces). API search engines and API directory services, such as ProgrammableWeb, Rapid API Hub, APIs.guru, and API Harmony, have been developed to facilitate the utilization of various APIs. Unfortunately, most API systems provide only superficial support, with no assistance in obtaining relevant APIs or examples of code usage. To better realize the “FAIR” (Findability, Accessibility, Interoperability, and Reusability) features for the usage of Web APIs, in this study, we developed an API inspection system (referred to as API Prober) to provide a new API directory service with multiple supplemental functionalities. To facilitate the findability and accessibility of APIs, API Prober transforms OAS (OpenAPI Specifications) into a graph structure and automatically annotates the semantic concepts using LDA (Latent Dirichlet Allocation) and WordNet. To enhance interoperability, API Prober also classifies APIs by clustering OAS documents and recommends alternative services to be substituted or merged with the target service. Finally, to support reusability, API Prober makes it possible to retrieve examples of API utilization code in Java by parsing source code in GitHub. The experimental results demonstrate the effectiveness of the API Prober in recommending relevant services and providing usage examples based on real-world client code. This research contributes to providing viable methods to appropriately analyze and cluster Web APIs, and recommend APIs and client code examples.
A Coverless Audio Steganography Based on Generative Adversarial Networks
Authors: Li Jing , Wang Kaixi , Jia Xiaozhu
Published: March 2023

Traditional audio steganography by cover modification causes changes to the cover features during the embedding of a secret, which is easy to detect with emerging neural-network steganalysis tools. To address the problem, this paper proposes a coverless audio-steganography model to conceal a secret audio. In this method, the stego-audio is directly synthesized by our model, which is based on the WaveGAN framework. An extractor is meticulously designed to reconstruct the secret audio, and it contains resolution blocks to learn the different resolution features. The method does not perform any modification to an existing or generated cover, and as far as we know, this is the first directly generated stego-audio. The experimental results also show that it is difficult for the current steganalysis methods to detect the existence of a secret in the stego-audio generated by our method because there is no cover audio. The MOS metric indicates that the generated stego-audio has high audio quality. The steganography capacity can be measured from two perspectives, one is that it can reach 50% of the stego-audio from the simple size perspective, the other is that 22–37 bits can be hidden in a two-second stego-audio from the semantic. In addition, we prove using spectrum diagrams in different forms that the extractor can reconstruct the secret audio successfully on hearing, which guarantees complete semantic transmission. Finally, the experiment of noise impacts on the stego-audio transmission shows that the extractor can still completely reconstruct the semantics of the secret audios, which indicates that the proposed method has good robustness.
Intelligent Computation Offloading Mechanism with Content Cache in Mobile Edge Computing
Authors: Li Feixiang , Fang Chao , Liu Mingzhe , Li Ning , Sun Tian
Published: March 2023

Edge computing is a promising technology to enable user equipment to share computing resources for task offloading. Due to the characteristics of the computing resource, how to design an efficient computation incentive mechanism with the appropriate task offloading and resource allocation strategies is an essential issue. In this manuscript, we proposed an intelligent computation offloading mechanism with content cache in mobile edge computing. First, we provide the network framework for computation offloading with content cache in mobile edge computing. Then, by deriving necessary and sufficient conditions, an optimal contract is designed to obtain the joint task offloading, resource allocation, and a computation strategy with an intelligent mechanism. Simulation results demonstrate the efficiency of our proposed approach.
A Network Intrusion Detection Method Based on Domain Confusion
Authors: Qu Yanze , Ma Hailong , Jiang Yiming , Bu Youjun
Published: March 2023

Network intrusion detection models based on deep learning encounter problems in the migration application. The performance is not as good as expected. In this paper, a network intrusion detection method based on domain confusion is proposed to improve the migration performance of the model. A domain confusion network is designed for feature transformation based on the idea of domain adaptation, mapping the traffic data in different network environments to the same feature space. Meanwhile, a regularizer is proposed to control the information loss in the mapping process to ensure that the transformed feature obtains enough information for intrusion detection. The experiment results show that the detection performance of the model in this paper is similar to or even better than the traditional models, and the migration performance in different network environments is better than the traditional models.
Experimental Thermal Behavior of Fibrous Structures for High-Performance Heat Resistant Fire Curtains
Authors: Cunha Diogo , Fangueiro Raul , Bessa João , Paiva Conceição , Ribeiro Daniel , Silva Elisabete , Silveira Dionísio , Soares Delfim , Vilarinho Cândida
Published: March 2023

Fibrous materials are often used in the manufacturing of fire protection devices such as fire curtains. Their optimization and improved performance is still a topic of interest. The present work aims to develop and test a new combination of fibers arranged in various 2D and 3D patterns with coatings. For this purpose, basalt fibers were added into a glass fiber fabric, and wires of a shape memory material (SMM) were inserted into the fabric to create air pockets induced by temperature. In fire curtains, the base structure is a 2D basket pattern, and all combinations were tested with and without a waterborne polyurethane (WPU) coating with inorganic materials. Three different tests were selected to characterize the thermal behavior: fire resistance, ignitability, and smoke production. Fiberglass proved to be the best material to provide thermal resistance in fire curtains, with the outer surface temperature of the fabric below 650 °C at the end of the tests. The SMM wires provided good protection during the initial stages of the test, but a combination of excessive deformation and reduced strength of the fabric resulted in a sudden failure of the structure. Basalt fibers contribute to a reduction of smoke production. It was observed an improvement of up to 10% in the thermal capacity between 1MIX2 (glass fibers fabric with coating, MIX2) and the best commercial curtain evaluated, Commercial3 (glass and steel fibers fabric with coating).
Two-Stage Optimal Scheduling of Highway Self-Consistent Energy System in Western China
Authors: Ye Yujiang , Shi Ruifeng , Gao Yuqin , Ma Xiaolei , Wang Di
Published: March 2023

Under the background of “carbon peaking and carbon neutrality goals” in China, the Highway Self-Consistent Energy System (HSCES) with renewable energy as the main body has become a key research object. To study the operational status of the HSCES in a specific region and realize the economically optimal operation of the HSCES, an HSCES model in a low-load, abundant-renewable-energy and no-grid scenario is established, and a two-stage optimal scheduling method for the HSCES is proposed. Moreover, in the day-ahead stage, uncertainty optimization scenarios are generated by Latin hypercube sampling, and a definition of the self-consistent coefficient is proposed, which is used as one of the constraints to establish a day-ahead economic optimal scheduling model. Through the case comparison analysis, the validity of the day-ahead scheduling model is confirmed and the optimal day-ahead scheduling plan is attained. Furthermore, in the intra-day stage, an intra-day rolling optimization method is proposed, which can effectively track the day-ahead scheduling plan and reduce the impact of forecast errors and energy fluctuations by coordinating the unit output within the HSCES system. It is verified that the HSCES can operate economically and safely in Western China, and self-consistently, without grid support.
A Study on the Control System of Electric Water Heaters for Decarbonization
Authors: Feyzioglu Ahmet
Published: March 2023

Greenhouse gas (GHG) emissions have significantly increased in recent years as a result of population rise and the increase in the number of residences, with high levels of energy use in homes and household appliances. It is crucial to move the housing industry away from high-carbon sources and toward low-carbon sources in order to minimize greenhouse gas emissions as a precaution. One of the most crucial systems that needs to be provided in order to achieve energy efficiency is the electric water heater (EWH), as they rank among the top electricity consumers. In this study, a double-tank EWH model was developed and simulated at various tank sizes (100 L, 200 L, 300 L and 400 L) and power ratios (1 kW, 2 kW, 3kW and 4 kW) in order to demonstrate energy efficiency. To obtain information for the simulation analysis of the tanks, the hourly water usage of 25 houses was measured. The single-tank and the double-tank models created for this study were both run in the Matlab/Simulink environment with an on-off controller applied, and their energy consumption was compared. Amounts were also determined based on how much energy both tanks consumed. It has been noted that the amount of GHG emissions is also reduced because the double tank uses less energy than the single tank does. The simulation showed that compared to the single tank, the dual tank produced 46.62% less GHG emissions at 45 W power and 47.51% less GHG emissions at 80 W.
Optimal Hierarchical Control for Smart Grid Inverters Using Stability Margin Evaluating Transient Voltage for Photovoltaic System
Authors: Pavon Wilson , Inga Esteban , Simani Silvio , Armstrong Matthew
Published: March 2023

This research proposed an optimal control approach for a smart grid electrical system with photovoltaic generation, where the control variables are voltage and frequency, which aims to improve the performance through addressing the need for a balance between the minimization of error and the operational cost. The proposed control scheme incorporates the latest advancements in heuristics and hierarchical control strategies to provide an efficient and effective solution for the smart grid electrical system control. Implementing the optimal control scheme in a smart power grid is expected to bring significant benefits, such as the reduced impact of renewable energy sources, improved stability, reliability and efficiency of the power grid, and enhanced overall performance. The optimal coefficient values are found by minimizing the cost functions, which leads to a more efficient system performance. The voltage output response of the system in a steady state is over-damped, with no overshoot, but with a 5% oscillation around the target voltage level that remains consistent. Despite the complexity of nonlinear elements’ behavior and multiple system interactions, the response time is fast and the settling time is less than 0.4 s. This means that even with an increase in load, the system output still meets the power and voltage requirements of the system, ensuring efficient and effective performance of the smart grid electrical systems.
Artificial Intelligence in Wind Speed Forecasting: A Review
Authors: Valdivia-Bautista Sandra Minerva , Domínguez-Navarro José Antonio , Pérez-Cisneros Marco , Vega-Gómez Carlos Jesahel , Castillo-Téllez Beatriz
Published: March 2023

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.
Outlook of Lithium-Ion Battery Regulations and Procedures to Improve Cell Degradation Detection and Other Alternatives
Authors: Cortada-Torbellino Maria , El Aroudi Abdelali , Valderrama-Blavi Hugo
Published: March 2023

This article constitutes a relatively new perspective that has emerged from the need to reduce environmental pollution from internal combustion engine vehicles (ICEVs) by reinforcing the fleet of electric vehicles (EVs) on the road. Future requirements to exclusively use zero-emission vehicles have resulted in the necessity of enhancing the testing and monitoring process for EVs in order to release reliable devices. The unpredictable response of lithium-ion batteries (LIBS), future lack of raw materials, and inconsistencies in the present regulations must be reviewed and understood in order to develop enhanced batteries. This article aims to outline the future perspective of nonconventional vehicles monopolizing the roads by year 2035 in order to eradicate CO2 emissions by year 2050.
A Study on Elemental Sulfur Equilibrium Content in Mixtures of Methane, Carbon Dioxide, and Hydrogen Sulfide under Conditions of Natural Gas Pipeline Transmission
Authors: Liu Gang , Hao Mengqi , Fan Shishui , Li Changjun
Published: March 2023

The effect of gathering pipeline pressure, temperature, and key components on the solubility of monomeric sulfur in high-sulfur-content natural gas is directly related to the prediction and prevention of sulfur deposition in surface gathering pipelines. Based on our previous study on a prediction model of sulfur solubility in gas with a new formula for the binary interaction coefficient between sulfur and H2S, a new gas–solid thermodynamic phase equilibrium solubility prediction model for monomeric sulfur in high-sulfur-content natural gas was improved based on the gas–solid phase equilibrium principle considering both physical and chemical solution mechanisms. Two new expressions for binary interaction coefficients between sulfur and CO2 and CH4, considering both temperature and solvent density, are proposed in this new solubility prediction model. In this paper, the main factors, such as the gathering pipeline pressure, gathering pipeline temperature, H2S, and CO2, affecting the solubility law of elemental sulfur in high-sulfur-content natural gas are investigated. The results show that the total solubility of elemental sulfur in high-sulfur-bearing natural gas tends to decrease with an increase in the gathering temperature, in which the increase in temperature promotes physical solution, and the physical solution mechanism prevails. Conversely, chemical solution is promoted, and the chemical solution mechanism prevails. With an increase in the gathering pressure, the total solubility of elemental sulfur in high-sulfur-content gas tends to increase, where the physical solubility decreases slightly at first and then increases continuously, with a pressure inflection point of about 2.0 MPa, and the pressure increase has a significant promoting effect on the chemical solubility of elemental sulfur. The increase in the H2S concentration promotes the solution of elemental sulfur in the gas phase in general and significantly promotes the chemical solution of elemental sulfur. The effect on elemental sulfur solubility can be neglected when the molar concentration of CO2 in the gas phase does not exceed 10%.
Emergency Board Management as a Tool for Strengthening Resilience of the Electric Power Industry: A Case Study in the Czech Republic
Authors: Karda Ladislav , Kavan Stepan
Published: March 2023

The study addresses the topic of disasters associated with the electricity sector in relation to strengthening its resilience as one of the basic elements of critical infrastructure. One of the important tools for the management of risks associated with the electricity sector is the activity and functioning of emergency boards, which are used during large-scale disasters. At the emergency board meeting, the current situation and the expected development of the event are assessed, possible solutions are proposed, and decisions are taken to coordinate activities at the strategic management level and to plan the disaster management process. Decision-making processes in the emergency board are often characterized by a high level of uncertainty in the incoming information. The case study and the research conducted are focused on the specific conditions of the Czech Republic. The purpose of the study is to assess the level of development of crisis-management plans with an emphasis on the electricity sector within the individual regions of the Czech Republic as a basic tool to strengthen the resilience of the electricity sector. Furthermore, it was examined whether there are standardized procedures for dealing with individual types of crisis situations and whether they are used as basic methodological support for the emergency board. This part of the investigation focused on a detailed examination of the use of each type of crisis situation in the conditions of the region. For the implementation of the actual investigation, general methods were used—analysis, synthesis, induction, deduction, and feedback methods. Furthermore, the questionnaire survey method was used to obtain information from crisis managers at the level of regions in the Czech Republic who are usually members of the emergency board. The sparse-scale evaluation method and Pareto analysis were used to process the information obtained. The survey itself yielded a large amount of interesting information on the level of processing of individual procedures for crisis situations, especially in relation to the electricity sector. Furthermore, the types of individual crisis situations were identified, where the procedures for dealing with them were processed with detailed analysis and details, and, on the contrary, disasters that are processed in a minimalist way.
A New QFD-CE Method for Considering the Concept of Sustainable Development and Circular Economy
Authors: Siwiec Dominika , Pacana Andrzej , Gazda Andrzej
Published: March 2023

The idea of sustainable development (SD) forces companies to combine the quality development of products with the simultaneous care of the natural environment. These actions should start with the product design process. The aim of the study was to create a modified method of Quality Function Development (QFD-CE), which will support the design of new products or improve the existing products on the market. In the proposed method (QFD-CE), the method integrates techniques such as: SMARTER method, brainstorming (BM), the method of selecting a team of experts, kinship diagram, fixed sum scale, and Likert scale. A novelty compared to the traditional QFD methodology is that design goals are set not only based on customer expectations, but also considering the impact on the natural environment. The originality of this proposition comes to the practical inclusion of including sustainability development criteria. The proposed method can be used in companies that design new products and are focused on caring for the natural environment. The QFD-CE method test method was performed for photovoltaic panels (PV). As part of the proposed QFD-CE method, the sequence of design activities was determined so that they meet customer expectations and can be simultaneously implemented according to the idea of SD. This method can be used for any product, mainly those that have a significant impact on the natural environment.
A Study on Capacity and State of Charge Estimation of VRFB Systems Using Cumulated Charge and Electrolyte Volume under Rebalancing Conditions
Authors: Jung Hyeonhong , Lee Seongjun
Published: March 2023

Extensive research has been conducted on energy storage systems (ESSs) for efficient power use to mitigate the problems of environmental pollution and resource depletion. Various batteries such as lead-acid batteries, lithium batteries, and vanadium redox flow batteries (VRFBs), which have longer life spans and better fire safety, have been actively researched. However, VRFBs undergo capacity reduction due to electrolyte crossover. Additionally, research on the capacity and state of charge (SOC) estimation for efficient energy management, safety, and life span management of VRFBs has been performed; however, the results of short-term experimental conditions with little change in capacity are presented without considering the rebalancing process of the electrolyte. Therefore, herein we propose a method for estimating the capacity of a VRFB using the cumulative charge and electrolyte volume amount under long-term cycle conditions, including rebalancing. The main point of the estimation method is to design a capacity estimation equation in the form of a power function with the measured cumulative charge of the battery as a variable and to update the initial capacity value applied to the estimation equation with the amount of electrolyte measured at the time of rebalancing. Additionally, the performance verification results of the SOC estimation algorithm using the capacity estimation model were presented using the long-term charge/discharge cycle test data of a 10 W-class single cell.
Discrete-Time Sliding Mode Current Control for a Seven-Level Cascade H-Bridge Converter
Authors: Comparatore Leonardo , Ayala Magno , Kali Yassine , Rodas Jorge , Pacher Julio , Renault Alfredo , Gregor Raúl
Published: March 2023

This paper deals with the implementation and performance analysis of discrete-time sliding mode (DTSM) current control applied to a seven-level cascade H-bridge converter to track three-phase reference currents for a reactive load. The converter output voltages are synthesized using a modulation scheme based on phase-shifted carrier modulation. Simulation and experimental tests have been added to demonstrate the performance of the proposed controller. At the same time, the effectiveness of the DTSM is verified under transient and steady-state conditions, respectively, by measuring the total harmonic distortion and the mean square error.
Transient Stability Analysis and Enhancement Techniques of Renewable-Rich Power Grids
Authors: Poulose Albert , Kim Soobae
Published: March 2023

New techniques and approaches are constantly being introduced to analyze and enhance the transient stability of renewable energy-source-dominated power systems. This review article extensively discusses recent papers that have proposed novel and innovative techniques for analyzing and enhancing the renewable source-dominated power system’s transient stability. The inherent low-inertia characteristics of renewable energy sources combined with fast-acting power electronic devices pose new challenges in power systems. Different stability concerns exist for grid-following and subsequent grid-forming converter/inverter connections to power grids; hence, distinct solutions for enhancing the transient stability have been devised for each. Moreover, the fundamental concepts and characteristics of converter/inverter topologies are briefly discussed in this study. Recent discussions and reviews of analysis and enhancement techniques in transient stability could lead to new ways to solve problems in power systems that rely primarily on renewable energy sources.