Electronics, Volume 13, Issue 7 (April-1 2024) – 243 articles

Named entity recognition (NER) is a critical subtask in natural language processing. It is particularly valuable to gain a deeper understanding of entity boundaries and entity types when addressing the NER problem. Most previous sequential labeling models are task-specific, while recent years have witnessed the rise of generative models due to the advantage of tackling NER tasks in the encoder–decoder framework. Despite achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. In this paper, a multiple attention framework is proposed which introduces the attention of entity-type embedding and word–word relation into the named entity recognition task. To improve the accuracy of entity-type mapping, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information input to the model via the encoder’s self-attention. To enhance the contextual information, we take the entity types as part of the input. Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross-attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word–word relations and extract the corresponding embedding into the cross-attention mechanism. Through word–word relation information, the method can learn and understand more entity boundary information, thereby improving its entity recognition accuracy. We performed experiments on extensive NER benchmarks, including four flat and two long entity benchmarks. Our approach significantly improves or performs similarly to the best generative NER models. The experimental results demonstrate that our method can substantially enhance the capabilities of generative NER models. Full article

Drainage pipes are a critical component of urban infrastructure, and their safety and proper functioning are vital. However, haze problems caused by humid environments and temperature differences seriously affect the quality and detection accuracy of drainage pipe images. Traditional repair methods are difficult to meet the requirements when dealing with complex underground environments. To solve this problem, we researched and proposed a dehazing method for drainage pipe images based on multi-attention multi-scale adaptive feature networks. By designing multiple attention and adaptive modules, the network is able to capture global features with multi-scale resolution in complex underground environments, thereby achieving end-to-end dehazing processing. In addition, we also constructed a large drainage pipe dataset containing tens of thousands of clear/hazy image pairs of drainage pipes for network training and testing. Experimental results show that our network exhibits excellent dehazing performance in various complex underground environments, especially in the real scene of urban underground drainage pipes. The contributions of this paper are mainly reflected in the following aspects: first, a novel multi-scale adaptive feature network based on multiple attention is proposed to effectively solve the problem of dehazing drainage pipe images; second, a large-scale drainage pipe data is constructed. The collection provides valuable resources for related research work; finally, the effectiveness and superiority of the proposed method are verified through experiments, and it provides an efficient solution for dehazing work in scenes such as urban underground drainage pipes. Full article

The paper presents a nonlinear field-oriented control technique based on the tensor product representation of the nonlinear induction machine model and the solvability of linear matrix inequalities. The nonlinear model has 32 quasi linear parameter-varying equivalent variants, and it is shown that only half of the models result in feasible controller. Two control goals are realized: torque control and speed control. The controller is a nonlinear state feedback controller completed by integral action. A new block diagram is investigated for speed control. The controller gains are designed by the solution of linear matrix inequalities to solve the Lyapunov inequality to obtain a stable and fast response and constraints on the control signal. The presented methods are verified and compared by simulations. Full article

When designing a system at the Electronic System Level (ESL), designers are confronted with a very large number of design decisions, each affecting the characteristics of the resulting system. Simultaneously, the demands for the system’s performance, reliability, and energy consumption have increased drastically. Design Space Exploration (DSE) aims to facilitate this complex task by automating the system synthesis and traversing the design space autonomously. Previous studies on DSE have mainly considered fixed architectures with a fixed set of hardware components only. In the paper at hand, we overcome this limitation to allow for a higher degree of freedom in the design of a multiprocessor system. Instead of a fixed architecture as input, we are using a resource library containing resource types whose instances can then be arbitrarily placed and connected. More specifically, we enable the exploration of the types, the number, and the positions of required processing-type instances in a grid-based topology template in addition to deciding on the remaining system synthesis tasks, namely, resource allocation, task binding, routing, and scheduling. We provide an extensible framework, based on Answer Set Programming (ASP) modulo Theories (ASPmT), for generating system architectures fulfilling predefined constraints. Our studies show that this higher degree of freedom, originating from fewer restrictions regarding the architecture, leads to an increased complexity of the problem. In extensive experiments, we show scalability trends for a set of parameters, demonstrating the capabilities and limits of our approach. Full article

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images. Full article

This paper investigates the potential of active reconfigurable intelligent surfaces (RIS) to enhance wireless-powered communication networks (WPCNs), addressing the evolving connectivity needs of the internet of things (IoT). Active RIS, capable of amplifying and reflecting signals, offers a solution to surpass the limitations of passive RIS, such as double-fading attenuation, aiming to significantly improve network throughput and coverage. Our research focuses on exploiting the amplification capabilities of active RIS to boost the overall network sum throughput, engaging in a comprehensive optimization of critical network parameters, including time allocation, reflection coefficients, and phase shift matrices specific to active RIS. The formulated problem is non-convex and highly complex due to the coupling of optimization variables. We employed a successive convex approximation algorithm to solve the throughput maximization problem by converting the non-convex constraints into approximated convex constraints and solving them iteratively. Through extensive simulations, we demonstrate that our active RIS-assisted network substantially outperforms networks facilitated by passive RIS, marking a significant advancement in WPCN performance. These findings underscore the potential of active RIS technology in realizing the full capabilities of IoT connectivity. Full article

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method. Full article

Detecting and identifying small infrared targets has always been a crucial technology for many applications. To address the low accuracy, high false-alarm rate, and poor environmental adaptability that commonly exist in infrared target detection methods, this paper proposes a composite infrared dim and small target detection model called USES-Net, which combines the target prior knowledge and conventional data-driven deep learning networks to make use of both labeled data and the domain knowledge. Based on the typical encoder–decoder structure, USES-Net firstly introduces the self-attention mechanism of Swin Transformer to replace the universal convolution kernel at the encoder end. This helps to extract potential features related to dim, small targets in a larger receptive field. In addition, USES-Net includes an embedded patch-based contrast learning module (EPCLM) to integrate the spatial distribution of the target as a knowledge prior in the training network model. This guides the training process of the constrained network model with clear physical interpretability. Finally, USES-Net also designs a bottom-up cross-layer feature fusion module (AFM) as the decoder of the network, and a data-slicing-aided enhancement and inference method based on Slicing Aided Hyper Inference (SAHI) is utilized to further improve the model’s detection accuracy. An experimental comparative analysis shows that USES-Net achieves the best results on three typical infrared weak-target datasets: NUAA-SIRST, NUDT-SIRST, and IRSTD-1K. The results of the target segmentation are complete and sufficient, which demonstrates the validity and practicality of the proposed method in comparison to others. Full article

In this paper, an n-i-p-type GaN barrier for the final quantum well, which is closest to the p-type GaN cap layer, is proposed for nitride light-emitting diodes (LEDs) to enhance the confinement of electrons and to improve the efficiency of hole injection. The performances of GaN-based LEDs with a traditional GaN barrier and with our proposed n-i-p GaN barrier were simulated and analyzed in detail. It was observed that, with our newly designed n-i-p GaN barrier, the performances of the LEDs were improved, including a higher light output power, a lower threshold voltage, and a stronger electroluminescence emission intensity. The light output power can be remarkably boosted by 105% at an injection current density of 100 A/cm² in comparison with a traditional LED. These improvements originated from the proposed n-i-p GaN barrier, which induces a strong reverse electrostatic field in the n-i-p GaN barrier. This field not only enhances the confinement of electrons but also improves the efficiency of hole injection. Full article

Online fashion retailers face enormous challenges due to high return rates that significantly affect their operational performance. Proactively predicting returns at the point of order placement allows for preemptive interventions to reduce potentially problematic transactions. We propose an innovative inductive Heterogeneous Graph Neural Network tailored for proactive return prediction within the realm of online fashion retail. Our model intricately encapsulates customer preferences, product attributes, and order characteristics, providing a holistic approach to return prediction. Through evaluation using real-world data sourced from an online fashion retail platform, our methodology demonstrates superior predictive accuracy on the return behavior of repeat customers, compared to conventional machine learning techniques. Furthermore, through ablation analysis, we underscore the importance of simultaneously capturing customer, order, and product characteristics for an effective proactive return prediction model. Full article

Reconfigurable intelligent surface (RIS)-assisted cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) technology exhibits significant potential in enhancing the energy efficiency of 6G mobile communications. Nevertheless, recent studies suggest that both access points (APs) and RISs encounter challenges related to a high energy consumption during operation. To address this issue, strategies involving AP hibernation and RIS shut-off are proposed. Subsequently, an optimization problem is formulated to jointly optimize RISs, beamforming vectors, and AP selection with the aim of maximizing the energy efficiency (EE). Initially, the non-convex optimization problem for maximizing energy efficiency is decomposed into three sub-problems. These sub-problems are subsequently reformulated using fractional programming and variational programming techniques and then solved using the successive convex approximation (SCA) algorithm, Dinkelbach algorithm, and greedy algorithm, respectively. Subsequently, an alternate optimization algorithm based on block gradient descent is introduced to iteratively solve the four-variable optimization problem, thereby obtaining an approximate solution to the original problem. The simulation results demonstrate that the algorithm significantly reduces energy consumption. Specifically, compared to the scheme without the hibernation strategy, the energy efficiency (EE) is enhanced by 35%. Full article

In recent years, the digitization of cultural heritage has been favored by significant advancements in specific technologies, such as photogrammetry and three-dimensional scanning. The digital representations of artifacts, paintings, books, and collections, as well as buildings or archaeological sites, has led to the transfer of cultural organizations to the digital space. On the other hand, the rapid development of immersive technologies and the Internet of Things is expected to decisively shape virtual cultural heritage in the coming years. However, this digital transition should expand its impact on most of the population. This article aims to cover the lack of structured methodology in the design and development of inclusive virtual spaces in cultural heritage. This research introduces a holistic framework that is mainly based on the disciplines of virtual museology. The proposed methodology takes into account the advancements in extended reality and the creative industry of computer games. The multisensory approach would lead to advanced immersive experiences, while the multilayered approach of cultural heritage content would enhance accessibility in inclusive virtual spaces. Moreover, this holistic framework could provide evidence from the virtual worlds that could be applied to real cultural heritage organizations. Full article

This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement. Full article

Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of Optical Character Recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degrees of document images, researchers have proposed a variety of solutions. In this paper, we have summarized some challenges and difficulties in the field of document image binarization. Approximately 60 methods documenting image binarization techniques are mentioned, including traditional algorithms and deep learning-based algorithms. Here, we evaluated the performance of 25 image binarization techniques on the H-DIBCO2016 dataset to provide some help for future research. Full article

Lung cancer, a prevalent and life-threatening condition, necessitates early detection for effective intervention. Considering the recent advancements in deep learning techniques, particularly in medical image analysis, which offer unparalleled accuracy and efficiency, in this paper, we propose a method for the automated identification of cancerous cells in lung tissue images. We explore various deep learning architectures with the objective of identifying the most effective one based on both quantitative and qualitative assessments. In particular, we assess qualitative outcomes by incorporating the concept of prediction explainability, enabling the visualization of areas within tissue images deemed relevant to the presence of lung cancer by the model. The experimental analysis, conducted on a dataset comprising 15,000 lung tissue images, demonstrates the effectiveness of our proposed method, yielding an accuracy rate of 0.99. Full article

A new decoding method for low-density parity-check (LDPC) codes is presented to lower the energy consumption of LDPC decoders for NAND flash-based storage systems. Since the channel condition of NAND flash memory is reliable for most of its lifetime, it is inefficient to apply the maximum-effort decoding with the full parity-check matrix (H-matrix) from the beginning of the lifespan. As the energy consumption and the decoding latency are proportional to the size of the H-matrix used in decoding, the proposed algorithm starts the decoding with a partial H-matrix selected by considering the channel condition. In addition, the proposed partial decoding provides various error-correcting capabilities by adjusting the partial H-matrix. Based on the proposed partial decoding algorithm, a prototype decoder is implemented in a 65 nm CMOS process to decode a 4 KB LDPC code. The proposed decoder reduces energy consumption by 93% compared to the conventional LDPC decoding architecture at maximum. Full article

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability. Full article

Leakage diodes cause deviations in the thermal drift of ultra-low-power two-transistor (2T) reference circuits, resulting in either convex or concave output voltages against temperature, depending on the reference transistor types (n-type/p-type). This paper investigates the combined application of the convexity and concavity properties exhibited by the output voltage of complementary 2T references, one n-type and one p-type. By exploiting the body bias effect, this approach mitigates variations in the output reference voltage caused by temperature fluctuations. Software optimization is also used to obtain the required aspect ratios after formulating the required criteria for drain-induced barrier lowering (DIBL) elimination in the first stage. The performance of the proposed reference is evaluated by post-layout Monte Carlo simulations. In the range of 0 °C to 100 °C, the output reference voltage has an average temperature coefficient (TC) of 26.7 ppm/°C without any temperature trim. The output reference voltage is 195.5 mV with a standard deviation of 13.6 mV. The line sensitivity (LS) is 17.1 ppm/V in the supply voltage range of 0.5 V to 2.1 V at 25 °C. At 25 °C and 0.5 V, the power consumption is 28.8 pW, increasing to a maximum of 1.3 nW at 100 °C and 2.1 V. Full article

Nowadays, fine-tuning has emerged as a powerful technique in machine learning, enabling models to adapt to a specific domain by leveraging pre-trained knowledge. One such application domain is automatic speech recognition (ASR), where fine-tuning plays a crucial role in addressing data scarcity, especially for languages with limited resources. In this study, we applied fine-tuning in the context of atypical speech recognition, focusing on Italian speakers with speech impairments, e.g., dysarthria. Our objective was to build a speaker-dependent voice user interface (VUI) tailored to their unique needs. To achieve this, we harnessed a pre-trained OpenAI’s Whisper model, which has been exposed to vast amounts of general speech data. However, to adapt it specifically for disordered speech, we fine-tuned it using our private corpus including 65 K voice recordings contributed by 208 speech-impaired individuals globally. We exploited three variants of the Whisper model (small, base, tiny), and by evaluating their relative performance, we aimed to identify the most accurate configuration for handling disordered speech patterns. Furthermore, our study dealt with the local deployment of the trained models on edge computing nodes, with the aim to realize custom VUIs for persons with impaired speech. Full article

Surface imperfections in steel materials potentially degrade quality and performance, thereby escalating the risk of accidents in engineering applications. Manual inspection, while traditional, is laborious and lacks consistency. However, recent advancements in machine learning and computer vision have paved the way for automated steel defect detection, yielding superior accuracy and efficiency. This paper introduces an innovative deep learning model, GDCP-YOLO, devised for multi-category steel defect detection. We enhance the reference YOLOv8n architecture by incorporating adaptive receptive fields via the DCNV2 module and channel attention in C2f. These integrations aim to concentrate on valuable features and minimize parameters. We incorporate the efficient Faster Block and employ Ghost convolutions to generate more feature maps with reduced computation. These modifications streamline feature extraction, curtail redundant information processing, and boost detection accuracy and speed. Comparative trials on the NEU-DET dataset underscore the state-of-the-art performance of GDCP-YOLO. Ablation studies and generalization experiments reveal consistent performance across a variety of defect types. The optimized lightweight architecture facilitates real-time automated inspection without sacrificing accuracy, offering invaluable insights to further deep learning techniques for surface defect identification across manufacturing sectors. Full article

The abilities of quantitative description of noise are restricted due to its origin, and only statistical and spectral analysis methods can be applied, while an exact time evolution cannot be defined or predicted. This emphasizes the challenges faced in many applications, including communication systems, where noise can play, on the one hand, a vital role in impacting the signal-to-noise ratio, but possesses, on the other hand, unique properties such as an infinite entropy (infinite information capacity), an exponentially decaying correlation function, and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window, putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criterion on which the main idea of this study is based on is the central limit theorem, which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. This study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.), whose output signals are analyzed according to criteria that encompass the probability density functions of the chaotic signal itself, its envelope, and its phase and statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. Thus, a statistical-characteristic-based algorithm, which includes a series of tests, was developed for a Gaussian-like signal assessment. Following the algorithm, the analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates a Gaussian distribution. This allows for the generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems. Full article

Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based on the predicted preferences using quality of service data and asymmetrically weighted cosine similarity. To address the cold start problem, we design a questionnaire to survey new user preferences and suggest personalized cloud providers accordingly. The experiments based on the Cloud Armor database demonstrate that our approach outperforms other models. The proposed approach has a precision of 85% and achieves a mean absolute error (MAE) of 0.05 and root-mean-square error (RMSE) of 0.14 for the differences between the input and predicted values. We also receive a satisfaction level of nearly 78.5% for recommendation lists provided to new cloud service customers. Full article

Mobile phone backplanes are an important part of mobile phones, and are often affected by a wide range of factors during the manufacturing process, resulting in defects of various scales and similar backgrounds. Therefore, accurately identifying these defects is crucial for improving mobile phone quality. To address this challenge, this paper proposes a multi-scale and dynamic attention fusion UNet (MDAF-UNet) model. The model innovatively combines normal convolution with dilated convolution. This allows the model to capture subtle features of defects and to perceive a larger range of feature variations. Moreover, an improved attention mechanism is introduced in this paper. It fuses channel attention and spatial attention, and dynamically adjusts the feature fusion strategy with learnable weights. This allows the model to increase the attention of important features and improve the effectiveness of feature representation. Experimental results on a publicly available dataset show that the MDAF-UNet model achieves 66.9% Mean Intersection over Union (MIoU), outperforming other state-of-the-art models. This result provides an effective solution to the mobile phone backplane defect segmentation problem. Full article

In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios. Full article

This article proposes a novel 1 bit electronically reconfigurable transmitarray, designed to facilitate digital two-dimensional beam scanning, boasting both high gain and a slim profile. The fundamental phase shifting unit of the transmitarray unit cell consists of a resonant cavity composed of a pair of orthogonal metal gates and dielectric layers, with a cross-sectional height of 0.17 λ. The middle layer of the phase-shifting unit is composed of circular gaps and C-shaped patches, and two diodes with opposite directions are loaded. By turning the diodes ON and OFF, current reversal is accomplished, allowing the unit to transition between its 0 and 1 states and achieve transmission-phase quantization. The unit’s minimal insertion loss is 0.37 dB in state 0 and 0.35 dB in state 1, respectively. In order to verify our design, we designed and processed a 16 × 16 transmitarray in the C-band. The simulated results are consistent with the experimental results. The experimental results show that the transmitarray can achieve ± 45° beam scanning on both the E-plane and H-plane, and the maximum gain is 20.59 dBi at 5 GHz, with an aperture efficiency of 20.5%. Full article

In this article, a 1 × 4 wideband, dual-polarized patch antenna array fed by a novel, differentially fed structure is proposed. The differentially fed structure of the antenna was realized by a parallel line structure that was printed on a PCB and connected with the inner and outer conductors of a coaxial cable. This method elaborately solved the problem of the narrow bandwidth of conventional microstrip differential feeding. By using a relatively thick air substrate (thickness = 0.19 λ₀), stacked patches, a coupling feeding structure, and a differential feeding structure with the novel design, the element of the patch antenna array introduced below operated from 0.415 GHz to 0.707 GHz (achieving the 52.0% bandwidth) with a VSWR < 2.0, yielding a high port isolation less than −28 dB. For the array, an active VSWR less than 2.0 was also obtained with a port isolation of less than −25 dB, ranging from 0.405 GHz to 0.696 GHz. In the desired bandwidth, the array had an azimuth 3 dB beamwidth of about 19° for both horizontal polarization and vertical polarization. The antenna array also had good performance in scanning (stable gain and 3 dB beamwidth) and circular polarization (a 3 dB axial ratio bandwidth better than 54.5%). Full article

This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an advanced web crawler, showcases the vast linguistic diversity and richness of English and Chinese, uniquely spanning the rarely covered news and music domains. Our methodical approach in web crawling and corpus assembly, coupled with rigorous experiments and manual evaluations, demonstrated its superiority by achieving high BLEU scores, marking significant strides in translation accuracy and model resilience. Its inclusion of these specific areas adds significant value, providing a unique dataset that enriches the scope for NMT research and development. With the rise of NMT technology, WCC-EC 2.0 emerges not only as an invaluable resource for researchers and developers, but also as a pivotal tool for improving translation accuracy, training more resilient models, and promoting interlingual communication. Full article

Unlike previous approaches in speech emotion recognition (SER), which typically extract emotion embeddings from a trained classifier consisting of fully connected layers and training data without considering contextual information, this research introduces a novel approach. It integrates contextual information into the feature extraction process. The proposed approach is based on the WavLM representation and incorporates a contextual transform, along with fully connected layers, training data, and corresponding label information, to extract single-lingual WavLM domain emotion embeddings (SL-WDEEs) and cross-lingual WavLM domain emotion embeddings (CL-WDEEs) for single-lingual and cross-lingual SER, respectively. To extract CL-WDEEs, multi-task learning is employed to remove language information, marking it as the first work to extract emotion embeddings for cross-lingual SER. Experimental results on the IEMOCAP database demonstrate that the proposed SL-WDEE outperforms some commonly used features and known systems, while results on the ESD database indicate that the proposed CL-WDEE effectively recognizes cross-lingual emotions and outperforms many commonly used features. Full article

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years. Full article

Clandestine assailants infiltrate intelligent systems in smart cities and homes for different purposes. These attacks leave clues behind in multiple logs. Systems usually upload their local syslogs as encrypted files to the cloud for longterm storage and resource saving. Therefore, the identification of pre-attack steps through log investigation is crucial for proactive system protection. Current methodologies involve system diagnosis using logs, often relying on datasets for feature training. Furthermore, the prevalence of mass encrypted logs in the cloud introduces a new layer of complexity to this domain. To tackle these challenges, we introduce CrptAC, a system for Multiple Encrypted Log Correlated Analysis, aimed at reconstructing attack chains to prevent further attacks securely. CrptAC initiates by searching and downloading relevant log files from encrypted logs stored in an untrusted cloud environment. Utilizing the obtained logs, it addresses the challenge of discovering event relationships to establish the attack provenance. The system employs various logs to construct event sequences leading up to an attack. Subsequently, we utilize Weighted Graphs and the Longest Common Subsequences algorithm to identify regular steps preceding an attack without the need for third-party training datasets. This approach enables the proactive identification of pre-attack steps by analyzing related log sequences. We apply our methodology to predict attacks in cloud computing and router breach provenance environments. Finally, we validate the proposed method, demonstrating its effectiveness in constructing attack steps and conclusively identifying corresponding syslogs. Full article