Miracle-dz/UniFAR-FTUs huggingface dataset:
Gain Analysis of Prototyped Wilson Current Mirror Using Double-Gate MOSFET

This research work designs and analyses a Wilson current mirror using Double-Gate (DG) MOSFET. It is suitable for biasing and constant current generation in analog design. The current analysis has been detailed for this device. The circuit simulation was performed using an electronic device simulator. Some of the benefits of independently driven double-gate MOSFET transistors in analog design have been demonstrated in this work. It allows dynamic threshold voltage adjusting; they are naturally suited to low-voltage operation.
A Super Pixel Segmentation Algorithm for SAR Images Based on APKSLIC

To improve the accuracy of Simple Linear Iterative Clustering (SLIC) algorithm in image segmentation, a superpixel segmentation algorithm based on adaptive parameters and Kuan filter mean estimation is proposed. This algorithm adaptively generates feature equalization parameter g based on the specific situation of the image, and then combines it with the average grayscale difference and spatial fluctuation difference constructed by Kuan filter estimation to improve the superpixel segmentation effect of SAR images. The experimental results show that compared with the other three superpixel segmentation algorithms, the proposed algorithm has a recall improvement of at least 0.7%, an undersegmentation error rate reduction of at least 5%, and an average running time reduction of at least 15%.
Catch You if Pay Attention: Temporal Sensor Attack Diagnosis Using Attention Mechanisms for Cyber-Physical Systems

In Cyber-Physical Systems (CPS), sensor data integrity is crucial since acting on malicious sensor data can cause serious consequences, given the tight coupling between cyber components and physical systems. While extensive works focus on sensor attack detection, attack diagnosis that aims to find out when the attack starts has not been well studied yet. This temporal sensor attack diagnosis problem is equally important because many recovery methods rely on the accurate determination of trustworthy historical data. To address this problem, we propose a lightweight data-driven solution to achieve real-time sensor attack diagnosis. Our novel solution consists of five modules, with the attention and diagnosis ones as the core. The attention module not only helps accurately predict future sensor measurements but also computes statistical attention scores for the diagnosis module. Based on our unique observation that the score fluctuates sharply once an attack launches, the diagnosis module determines the onset of an attack through monitoring the fluctuation. Evaluated on high-dimensional high-fidelity simulators and a testbed, our solution demonstrates robust and accurate temporal diagnosis results while incurring millisecond-level computational overhead on Raspberry Pi.
Design of a C Band High Efficiency Doherty Power Amplifier for 5G Applications

In the context of emerging 5G technology, there has been a notable development of specific power amplifiers capable of operating at high frequencies. Among the various amplifier architectures utilized, the Doherty power amplifier (DPA) stands out as a prominent choice. Through the use of parallel Class-AB and Class-C amplifiers, the DPA achieves improved performance, especially during back-off operation condition. Specifically designed for digitally modulated signals with a high peak-to-average power ratio (PAPR), a feature relevant to 5G systems, this study presents a highly efficient symmetrical DPA. The DPA configuration comprises two GaN devices of equal size, designated for the principal Class-AB and ancillary Class-C amplifiers, respectively. According to simulation results, the designed DPA shows a saturated output power (Psat) of 25 W within the frequency range of 4.8-5.2 GHz, accompanied by a drain efficiency spanning from 58.8% to 61.2% at peak operation and 39% to 40.7% at a 6 dB back-off.
Genetic Algorithm based Multi-objective optimization of SMPM

The present work deals with the optimization of an in-wheel surface mounted permanent magnet motor (SMPM) with an outer rotor and concentrated windings. At first, it is necessary to determine the equations dealing with the analytical model. To find the optimum design geometry, two objective functions were used. The first leads to maximize the efficiency of the machine. The second it is to minimize the machine weight. Additionally, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, the fast elitist multi-objective Genetic Algorithm (NSGA-II) and the cell model of the genetic algorithm (Mocell) are applied. The results obtained by both algorithms were compared and analyzed.
KUKA Robot Language Parser for Product Adaptive Manufacturing

The work reported in this paper enables to upload and activation of new programs to KUKA industrial robots during active operation, which would not be possible normally. By doing this it is possible to program those robots offline resulting in increased flexibility for modern manufacturing. The programs could be placed on work pieces to form a work-piece-driven production. Comparable projects increased the capabilities of robot programming by avoiding the KUKA Robot Language using high-level programming languages. Those approaches have proven themselves in the field of research. Nevertheless, this work tries a completely different approach by enabling offline programming and switching between programs while maintaining KRL the most used robot programming language for KUKA robots.
Rehabilitation Assistant - a Toolkit for Maintaining Health and Improving the Reliability of Personnel at Critical Infrastructure Facilities

The article is devoted to the development of medical transformative technology and the study of the possibility of applying the type-variety principle, a systematic analysis of the influence of the human factor and personnel errors in ensuring the highly reliable functioning and safety of critical systems. Minimizing staff errors and monitoring the health of employees at critical infrastructure facilities in the face of new challenges associated with the COVID-19 pandemic and barbaric military aggression can be achieved through the joint use of a variety of physical practices and exercises that affect all components of human health. Based on expert analysis of video images of a person and his movements, which are recorded periodically, strategies are formed to influence both his physical and psychological state, as well as his social and communicative activity, which thereby ensures the reliability, safety and sustainability of the functioning of critical systems
Automatic Detection and Analysis towards Malicious Behavior in IoT Malware

Our society is rapidly moving towards the digital age, which has led to a sharp increase in IoT networks and devices. This growth requires more network security professionals, who are focused on protecting IoT systems. One crucial task is to analyze malicious software to gain a deeper understanding of its functionalities and response methods. However, malware analysis is a complex process that requires the use of various analysis tools, including advanced reverse engineering techniques. For beginners, parsing complex binary data can be particularly challenging as they may be strange with these tools and the basic principles of analysis. Even for experienced analysts, understanding reverse engineering binary files and assembly lists is daunting.Facing these challenges, we propose a two-fold solution. Firstly, we create a detailed list of analysis tools and construct a malware analysis framework aimed at simplifying the analysis process. The framework will list the key data points that need to be addressed in the analysis, providing analysts with the tools and information needed for effective malware analysis. Secondly, we will demonstrate that advanced analysis techniques by providing analysis scripts which automate the reverse engineering process in malware analysis. To evaluate the accuracy of our behavior classification system, we will use our framework and analysis scripts to analyze known malware samples. Then, we will compare the accuracy of script-based analysis results and evaluate their ability to identify malicious software behavior. Our research results indicate that by following our framework and using our scripts, we can detect over 80% critical malware behaviors in known samples, which highlights the potential of simplifying the process of malware analysis, making it easier to learn and implement.
Exploiting Local Information with Subgraph Embedding for Graph Neural Networks

Graph neural networks (GNNs) have shown success in various data mining tasks, including graph classification. Yet, as GNNs progress through deeper layers, they face a significant challenge: the dilution of local information across the graph. This is especially problematic for graph classification tasks, which integrate node features to produce a single graph embedding. In this paper, we introduce a subgraph embedding method for GNNs that aims to balance global and local information, thereby mitigating information dilution. Our method explicitly partitions a graph by removing edges between nodes, selecting subgraphs based on the minimal external degree. This method ensures limited information exchange and maintains local node characteristics, even with additional layers. An algorithm considers subgraph-level characteristics to strike a balance between global and local information. Multiple independent GNNs then process these subgraphs, and their features merge for graph classification. Rigorous testing on 11 benchmark datasets against 14 leading methods reveals that our method excels in 95 out of 154 instances, achieving a peak performance increase of 5.4%p. Notably, with increasing layers, our method consistently outshines traditional techniques, underlining its prowess in countering information dilution in GNNs.
JP-DouZero: an enhanced DouDiZhu AI based on reinforcement learning with peasant collaboration and intrinsic rewards

DouDiZhu is a popular Chinese three-player poker game where two peasants collaborate against a landlord. The high complexity of the problem for a reinforcement learning-based AI is attributed to three factors: imperfect information, coexistence of competition and cooperation, as well as huge state and action spaces. The current state-of-the-art system is called DouZero, which combines Monte Carlo methods with deep neural networks and makes use of self-play without human expertise. This paper proposes JP-DouZero which addresses two shortcomings of existing methods, namely: a) the cooperation between the two peasants is not explicitly modeled and b) ’sparse reward’ i.e., state-action trajectories receive a binary score based on whether they lead to win or loss at the end of the game. For the former, we design a joint peasant Q-network to determine the reward of every state-action pair from the standpoint of the peasant coalition. For the latter, we devise a new reward mechanism comprising of three parts, namely curiosity-driven reward, result-driven reward, and extrinsic reward. Extensive experiments corroborate a significant increase of the peasant advantage in terms of a 2.2% higher winning rate and over 0.12 higher difference of scored points compared with DouZero baseline. An ablation study is carried out to show the impact of the design choices on the overall performance improvement.
Information extraction of Chinese medical electronic records via evolutionary neural architecture search

To obtain valuable information from the Chinese electronic medical records (CEMR), information extraction (IE), including named entity recognition (NER) and relation extraction (RE), is often used to extract information from semi-structured text. Recently, machine learning (ML) and deep learning (DL) models for IE have been successfully applied to CEMR with positive results. However, the performance of ML and DL relies on extensive expert knowledge and manual parameter tuning. In this paper, we propose a new method based on an evolutionary algorithm to tackle the network structure and training parameters selection problem of BiLSTM for NER and RE. In our method, an efficient mixed-length genetic encoding strategy is designed to represent the BiLSTM network information and the model training parameters. Specifically, all learning rates, num of hidden layers and neurons, and optimizers can be designed automatically, and then an optimal set of selection results can be obtained. In addition, we design efficient genetic operators to adapt to hybrid parameters and propose a deep-learning strategy to reduce computational overhead. The experiments show that our method achieves 89.13% and 60.85% F1 scores on the CCKS2019 Task 1 and CHIP-2020 Task 2 datasets, respectively, which demonstrates the effectiveness of our method.
Artificial Intelligence Approach in the Detection of Lung Diseases Developing Post-COVID-19 with Lung Images

Although it is known that the COVID-19 process is over, the subsequent damage caused by COVID-19 on the body is undeniable. The SARS-CoV-2 virus has been shown to be responsible for causing acute respiratory distress in a large number of COVID-19 cases. When the literature is reviewed, Thorax Computed Tomography is recommended to evaluate permanent lung damage in individuals recovering from COVID-19. When tomography images are examined, the respiratory systems of patients who have had COVID-19 are significantly affected by the virus. In the study, thorax CT images of patients who had COVID-19 and then came back to the hospital and were diagnosed with COPD and Bronchiectasis were examined with artificial intelligence methods. The study consists of two stages. First, COVID-19, COPD, and Bronchiectasis datasets were trained with pre-trained deep-learning models. Then, only the COVID-19 dataset was trained with the GAN algorithm, and the model weights were recorded. Tests were performed on the COPD and Bronchiectasis dataset with the recorded model weight. The performance metric ratios of the training results obtained with the COVID-19 dataset and test trained individually and the combination of three classes was compared in the test environment. As a result of the proposed study, the highest performance rate for the classification method made in the first step was found to be 99% in the pre-trained DenseNet201 model. In the second step of the study, model weights were recorded on the COVID-19 dataset trained with the GAN algorithm and tested on COPD and Bronchiectasis datasets. Lower loss rates are observed in the GAN algorithm compared to the models in the first stage of the study.
Analysis of Switched Reluctance Motor based on RMXprt, Maxwell2D and Matlab

This paper gives a performance analysis of a 4-phase, 550W, 8/6 poles, switched reluctance motor (SRM). RMXprt (analytical software) built with Maxwell 2D (finite element software) was used for the simulation and analysis of this motor. Since non-linear features of this kind of motor lead to complicating the analysis methodologies, so finite element analysis (FEA) was employed. Results of the motor such as efficiency, output power, output torque, inductance, phase current, induced voltage, and flux linkage, were determined by RMXprt. Maxwell2D software results of current and flux linkage were compared with Matlab/Simulink with an appropriate error rate.
Speech Emotion Recognition using Long Short-Term Memory Models and Grey Wolf Optimization

Machine learning and speech emotion detection are two rapidly developing fields that majorly impact human-centered computing. Machine learning allows computers to learn from data and make predictions, while speech emotion detection allows computers to identify and understand human emotions from speech. These two technologies create a wide range of new and innovative human-computer interaction (HCI) applications. As machine learning and speech emotion detection technologies continue to develop, they will play an increasingly important role in human-centered computing. These technologies will enable us to create more natural, intuitive, and engaging HCI experiences that meet user needs better. Deep learning algorithms can learn high-level features directly from raw data. This has led to the development of new emotion recognition approaches that use deep learning models. These models are trained on advanced representations of speech, such as spectrograms and time-frequency representations. This work proposes a LSTM model with GWO Optimization, which ensures the best parameters for the model to be the most accurate for the given set of parameters. The performance of the proposed model was tested on a dataset created by combining three well-known datasets - RAVDESS, SAVEE, and TESS. The model achieved 75% test accuracy, and when combined with a GWO optimizer, it was able to achieve 82.3% accuracy
Challenges of Profiling Offenders for Recidivism Risk

Recidivism is a significant challenge for society and the authorities. Identifying the factors that lead to re-offending is critical, and AI can assist in recognizing hidden patterns. Through training on existing databases of previous offenders, AI models can predict potential re-offence in those currently incarcerated. To effectively reduce re-offence rates and eliminate crime, assessing the risk and implementing appropriate countermeasures and rehabilitation programs is essential. We utilize machine learning algorithms for predicting recidivism rates, focusing on achieving the best Accuracy scores for the NIJ dataset (National Institute of Justice). To improve the correctness of our predictions, we conduct separate experiments for each gender and crime group, as they exhibit distinct statistics and characteristics. Additionally, we implement three feature selection methods to reduce the number of features by 45%, resulting in an 11% increase in Accuracy for one data subset with a nominal difference for most subsets. The best average Accuracy for the original dataset was 68%, which is the same after selected features provided by the LightGBM model. The most crucial feature that repeats in most experiments is the age at release, between 18 and 22, which shows young offenders exhibit a higher risk of recidivism. The results show inconsistent differences in gender, location, and type of crime committed. These variations can lead to biased outcomes, hindering the possibility of a universal solution for all situations. Additionally, attaining improved results and a scalable, generalized solution poses a challenge. Similarly, the lack of crucial personal information from data such as mental health, drug abuse, employment, and education results in higher importance factors of indirect causes of recidivism obstructing the determining factors.
Explainable Multi-Modal and Local Approaches to Modelling Injuries in Sports Data

To tackle the difficult task of predicting injuries, initial attempts have been made to apply machine learning methods to complex, multivariate sports data. However, no machine learning model has emerged which has good performance and generalisability to multiple datasets. Focusing on explainable machine learning, this paper discusses possible limitations in common approaches to injury prediction, and investigates methods that might be able to overcome these limitations. In particular, we first examine the value that can be obtained by including daily records of psychological variables to provide a multi-modal perspective on athletes. Secondly, we consider whether models that focus on learning from local rather than global patterns in the data are better suited to modelling injuries. We explain how the diversity of injuries in terms of severity, duration, type (chronic versus acute) and context may make local models preferable. The local modelling approaches we consider include subgroup discovery, anomaly detection and casebased classification. Since the proposed approaches threaten to increase the model complexity, we take great care to ensure that explainability is maintained, and describe the ways in which each of our methods is explainable. This leads to a set of explainable, local models. To provide an empirical basis for the investigations, we perform a series of computational studies using data obtained by daily recording of professional association football players in the Netherlands over a period of two seasons. These studies aim to expand the currently limited literature on applying explainable ML methods to injury prediction. Our results indicate that predicting injuries is very difficult in general, while the focus of this paper is mainly on providing insights on explainability and interpretability. Therefore, alongside results detailing the performance of various machine learning models, we also include multiple examples of explanations that can be extracted using our proposed methods, which may support actionability.
Research on Combined Detection Method of Partial Discharge Based on UHF and X-ray Imaging

In equipment such as GIS, switch cabinets, and transformers, there is an imperative for real-time monitoring to detect potential defects, especially during power protection phases. This study introduces a novel detection approach that synergizes ultra-high frequency (UHF) time-difference positioning with X-ray scanning imaging, specifically for identifying partial discharges. The efficacy of this combined method has been corroborated through rigorous on-site case analyses. Such a method holds significant promise for continuously tracking and monitoring potential flaws in substation hardware and primary power protection apparatuses, offering real-time insights into equipment health and its trajectory. Consequently, this approach furnishes a foundational reference for informed decisions regarding equipment maintenance and status evaluations.
Smart Building IoT Cybersecurity: A Review of Threats and Mitigation Technique

The popularity of smart buildings is increasing, thanks to their ability to enhance energy efficiency and provide comfortable environments for their occupants. However, with this increase in Internet of Things (IoT) technology comes a higher risk of cybersecurity threats. These threats can leave intelligent buildings vulnerable to attacks, so this research paper aims to identify their security risks comprehensively. Additionally, it provides various measures to strengthen security protocols against physical attacks, network-based incursions, and malicious assaults. These measures include access control, network segmentation, regular patching, and threat monitoring, all of which contribute to decreasing the susceptibility of intelligent buildings to cyber-attacks. Consequently, occupants and assets are protected from potential harm or loss. As we progress, it is essential to consistently prioritize IoT cybersecurity, which will continue to evolve through further research efforts to ensure continued effectiveness over time. [1]
Neural Network-Based Log Analysis Methods for 5G Network

Log analysis plays a crucial role in understanding system behavior and detecting anomalies in various domains. This article presents the results of our autoencoder type neural network trained for log analysis and anomaly detection based on the reconstruction error of each entry. Furthermore, the article focuses on the challenge of text vectorization, specifically employing a bag-of-words method for representing log messages in a numerical format suitable for neural network models. The bag-of-words approach captures the frequency-based representation of log messages. The study also acknowledges the limitations of this method in capturing semantic information. It is important to note that this study represents only a starting point in the journey of log analysis and text vectorization. The field encompasses a wide range of existing working methods, and further research and exploration are necessary to uncover the full potential of log analysis techniques for various applications.