Detection System Research Articles

Enhancing resilience and security in the U.S. power grid against cyber-physical attacks

The U.S. power grid faces increasing risks from cyber-physical attacks that could disrupt essential services and compromise national security. The fusion of artificial intelligence with cybersecurity protocols is perceived to present a consequential shift in the current efforts to safeguard critical infrastructures. This manuscript aims to identify the most critical vulnerabilities within the power grid's infrastructure, develop advanced machine learning-based threat detection systems, and propose automated response mechanisms to mitigate impacts effectively. By integrating comprehensive vulnerability assessments, innovative detection technologies, and autonomous response strategies, this study seeks to enhance the resilience of the power grid against sophisticated cyber threats.

Challenges and strategic solutions in commissioning and start-up of subsea production systems

The commissioning and start-up of subsea production systems represent critical phases in offshore oil and gas operations, where meticulous planning and execution are essential for success. This abstract presents an overview of the challenges encountered during these phases and proposes strategic solutions to address them effectively. Environmental factors such as deepwater operations and subsea geology pose significant challenges to commissioning and start-up activities. Technical challenges include ensuring equipment reliability and seamless integration of control systems in harsh underwater conditions. Operational challenges such as remote monitoring and intervention and personnel safety further compound the complexities of these operations. Strategic solutions to mitigate these challenges include advanced planning and simulation using digital twin technology and scenario planning. Enhanced risk management strategies, including contingency planning and early detection systems, are vital to anticipate and manage potential issues effectively. Collaborative partnerships between stakeholders, including suppliers and cross-disciplinary teams, foster innovation and enhance project resilience. Drawing from successful case studies and lessons learned, this abstract emphasizes the importance of proactive planning, risk mitigation, and collaborative approaches in ensuring the successful commissioning and start-up of subsea production systems. By implementing these strategic solutions, operators can enhance operational efficiency, minimize downtime, and improve safety outcomes, ultimately contributing to the long-term viability and sustainability of offshore energy production.

Multimodal emotion recognition from audio and video

As humans we want to interact with a machine as we would with a person, in a way that it understands us, advises us, and looks after us with no human supervision. Current systems lack empathy and user understanding in spite of very effective logical reasoning. By predicting the emotions of the users, we are able to identify their needs and cater to them as best as possible. Emotion recognition in video and audio has many potential applications, including conversational agents, recommendation systems as well as systems for smart homes, mental illness care, virtual reality games, remote physical training, education and car-hailing services. The aim of the project is to develop an automatic emotion detection system based on voice and facial expression. We propose a model that highlights contextual, multimodal information for emotion detection and recognition. If systems can understand emotions and respond accordingly to behavioral patterns, we can anticipate artificial agents becoming our cognitive-consulting partners in our daily lives. Additionally, the project can be expanded to make interactions more natural and better suited to handle complex situations.

Accelerated Screening of Alternative DNA Base-Organic Molecule-Base Architectures via Integrated Theory and Experiment.

Organic molecule-mediated noncanonical DNA self-assembly expands the standard DNA base-pairing alphabets. However, only a very limited number of small molecules have been recognized as mediators because of the tedious and complicated experiments like crystallization and microscopy imaging. Here we present an integrative screening protocol incorporating molecular dynamics (MD) for fast theoretical simulation and native polyacrylamide gel electrophoresis for convenient experimental validation. Melamine, the molecule that was confirmed mediating noncanonical DNA base-pairing, and 38 other candidate molecules were applied to demonstrate the feasibility of this protocol. We successfully identified seven stable noncanonical DNA duplex structures, and another eight novel structures with sub-stability. In addition, we discovered that hairpins at both ends can significantly stabilize the noncanonical DNA structures, providing a guideline to design small organic molecule-incorporated DNA structures. Such an efficient screening protocol will accelerate the design of alternative DNA-molecule architectures beyond Watson-Crick pairs. Considering the wide range of potential mediators, it will also facilitate applications such as noncovalent, highly dense loading of drug molecules in DNA-based delivery system and probe design for sensitive detection of certain molecules.

Towards a portable setup for the on-site SERS detection of miRNAs

The actual implementation of on-site analysis of biomarkers, such as miRNAs, depends on the availability of portable and easy-to-handle detection systems that can be used as point-of-care in real life applications. In this work, an automatized microfluidic platform combined with a portable Raman spectrometer is reported and tested for miR-214 detection by Surface Enhanced Raman Scattering (SERS). A multi-chamber SERS-active chip, functionalized according to a previously optimized two-step hybridization assay, was automatically incubated with the analyte solution. Preliminary tests allowed to adjust the portable Raman spectrometer acquisition conditions and to compare the obtained results with those of a bench Raman microscope. Finally, miR-214 at different concentrations was analyzed through an automatic procedure, achieving a limit of detection (LOD) in the picomolar range.

Remote intelligent perception system for multi-object detection

IntroductionDuring the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.MethodsAdvances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.ResultsThe performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.DiscussionFurthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Retraction Note: A novel system for automatic detection of K-complexes in sleep EEG

Retraction Note: A novel system for automatic detection of K-complexes in sleep EEG

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology’s detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Online boxplot derived outlier detection

AbstractOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks

AbstractAdvancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field.

X-ray fluorescence detection system for trace elements in oil based on graphite curved crystal technology

X-ray fluorescence detection system for trace elements in oil based on graphite curved crystal technology

Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector

ABSTRACT Addressing the pressing challenge of insurance fraud, which significantly impacts financial losses and trust within the insurance industry, this study introduces an innovative automated detection system utilizing ensemble machine learning (EML) algorithms. The approach encompasses four strategic phases: 1) Tackling data imbalance through diverse re-sampling methods (Over-sampling, Under-sampling, and Hybrid); 2) Optimizing feature selection (Filtering, Wrapping, and Embedding) to enhance model accuracy; 3) employing binary classification techniques (Bagging and Boosting) for effective fraud identification; and 4) applying explanatory model analysis (Shapley Additive Explanations, Break-down plot, and variable-importance Measure) to evaluate the influence of individual features on model performance. Our comprehensive analysis reveals that while not every re-sampling technique improves model performance, all feature selection methods markedly bolster predictive accuracy. Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. This study not only advances the theoretical framework for combating insurance fraud through AI but also provides a practical blueprint for insurance companies aiming to incorporate advanced AI strategies into their fraud detection arsenals, thereby mitigating financial risks and fostering trust systems.

Passive millimeter wave imaging low altitude detection technology

Passive millimeter wave imaging refers to the passive detection of naturally occurring background millimeter waves. After receiving external millimeter wave thermal radiation signals, the passive millimeter wave detection system will form images based on temperature differences and detect targets. Passive millimeter wave imaging has the advantages of non-radiation, non-contact, perspective imaging, good concealment, small size, and low power consumption. It is widely used in safety inspections, aircraft landing, low visibility navigation, sea surface detection and other fields. Driven by high and new technology, passive millimeter wave imaging low-altitude detection technology is rapidly developing. This paper introduced the basic principle of passive millimeter wave imaging, including the characteristics of millimeter wave radiation and Black-body radiation law, as well as its comparison with ordinary optical radiation. It also described the basic model of current passive millimeter wave imaging low altitude detection technology, explored the shortcomings of this technology and pointed out further work as well.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Anomaly Detection for Video Surveillance

Anomaly detection in video surveillance is critical for identifying abnormal behaviours or events that deviate from typical patterns. This abstract explores various techniques used to detect anomalies in surveillance videos, including traditional methods like motion detection and background subtraction, as well as advanced approaches such as deep learning and anomaly scoring algorithms. By leveraging these techniques, surveillance systems can automatically detect suspicious activities such as trespassing, theft, or violence, enabling timely intervention and improved security measures. Highlighting the challenges in anomaly detection, such as dealing with complex scenes, occlusions and discusses ongoing research efforts to enhance the accuracy and efficiency of anomaly detection systems in real-world surveillance scenarios. Key Words: anomaly, suspicious, pattern, motion, detection, security measure

Deep Learning Approaches for Hate Speech Detection

The main goals of this project are to develop reliable hate speech identification models that can recognize derogatory terminology, prejudiced attitudes, and damaging stereotypes on a variety of internet platforms. To help the models train and generalize efficiently, the study focuses on using big datasets with a variety of content types, including both hate speech and non-hate speech. The findings of this study suggest that machine learning has the potential to mitigate the negative consequences of hate speech by using automated filtering and flagging tools. The study also highlights the need for continued research and development to improve the accuracy, uniformity, and transparency of hate speech detection systems, and ultimately to foster a safer online environment to encourage all people.

Machine Learning for Intrusion Detection: A Reproducible Baseline is All You Need

Ensuring Responsible AI practices is paramount in the advancement of systems founded upon machine learning (ML) principles, particularly in sensitive domains like intrusion detection within cybersecurity. A fundamental aspect of Responsible AI is reproducibility, which guarantees the reliability and transparency of research outcomes. In this paper, we address the critical challenge of establishing reproducible for intrusion detection utilizing ML techniques. Leveraging the NSL-KDD dataset and the Edge-IIoTset, we carry out extensive experiments to evaluate the efficacy of our approach. Our study prioritizes meticulous experiment design and careful implementation setups, aligning with the principles of Responsible AI. Through rigorous experimentation and insightful discussions, we underscore the importance of reproducibility as a cornerstone in ensuring the resilience and reliability of intrusion detection systems. Our findings offer valuable insights for researchers and practitioners striving to develop Responsible AI solutions in cybersecurity and beyond. The source code is publicly accessible at https://github.com/Salma-00/Machine-Learning-for-Intrusion-Detection.

Exploring on Cloud Security Landscapes:A Comprehensive Review

Nowadays, the massive growth of data and the adoption of the cloud-based architecture indicate that the secure storage of data is a very essential issue. Cloud storage is the solution to convenient, scalable and cost-effective storage, yet security issues persist, especially regarding the protection of sensitive information. The encryption plays a vital role in which data is made unreadable and only accessible by those with the proper authorization. This helps in data management and minimizes data leaks. Shared responsibility in cloud security entails collaboration between the operators and users through the encryption strategy and a number of security measures to protect the data, apps, and infrastructure. This paper deals with the different cryptographic techniques and technologies used for improving cloud security. It discusses the notion of Non-Deterministic Cryptographic Scheme (NCS), which is a secure technique that effectively utilizes randomness in cryptographic algorithms. Regarding the Intel architecture compatibility, SGX and blockchain technology implementation not only ensures data immutability, but it also strengthens the data against cyber threats. SGX produces secure compartments inside cloud platforms, while blockchain makes data integrity possible through decentralized and immutable ledger technology. The research gives an impression of the seaside and multi sided cloud security. Involving encryption, access control, data loss prevention, and intrusion detection systems. Technologies of cryptography like Group Key Based Attribute Encryption (GKBAE) and Modified Random Fibonacci Cryptography (MRFC) offers both data protection and reduced resource requirement in clouds. GKBAE enables fine-grained access control and attribute-based encryption that ensures high data security without any additional computation. In addition, the paper also talks about the privacy-preserving technology, and policy-oriented access control framework. Techniques like Linear Elliptical Curve Digital Signature (LECDS) that combine digital signatures with blockchain technologies can also be used to implement the above features. To sum up, the changing scenario of cloud security requires ceaseless innovation and application of the latest cryptographic methods. Through encryption, hardware level security features and distributed ledger technology implementation, data environment is more strengthened and vulnerabilities to hacking, data leakage, and unauthorized access are decreased. Making use of the modern security tools not only does the data safety but also is responsible for the trustworthiness and stability of the cloud computing infrastructure. Keywords— Cloud Security, Encryption, Data Protection, Non-Deterministic Cryptographic Scheme (NCS), Intel SGX, Blockchain, Group Key Based Attribute Encryption (GKBAE), Modified Random Fibonacci Cryptography (MRFC), Data Confidentiality.

Concrete spalling detection system based on semantic segmentation using deep architectures

This paper presents a method for detecting the location of spalling and assessing the severity level of the spalling in concrete surfaces. The proposed method is constructed based on deep learning architectures and multi-class semantic segmentation. The proposed method can detect each pixel as a non-spalling, a deep-spalling, or a shallow-spalling. The proposed method consists of three different deep learning architectures with several encoders as backbone networks. Both qualitative and quantitative analyses show that the deep learning architecture with a certain encoder network can detect spalling with different severity levels very well. Additionally, the paper proposes a method to analyze the deep spalling areas of concrete to show their severity levels. The performance analysis shows that this approach provides very convincing results with respect to the actual affected spalling areas. The results convey that this paper achieved a higher level of performance for detecting spalling and assessing the severity of the spalling.

In situ Lithium Analysis In MgLi Alloys Using Laser Induced Breakdown Spectroscopy with a Compact Chamber.

This study explores the feasibility of in situ Lithium (Li) analysis in Magnesium-Lithium (MgLi) alloys using Laser-Induced Breakdown Spectroscopy (LIBS). It focuses on two Li emission lines: Li I 670.8 nm (resonance) and Li I 610.4 nm (non-resonance). Comparing charateristics at atmospheric and low pressures, self-reversal signature are observed in both emission lines at atmospheric pressure, complicating the analysis. Challenges in suppressing self-reversal effect using laser energy and detection window adjustments are noted. To address this, a compact chamber (80 mm × 50 mm × 50 mm) with adjustable pressure (using a portable vacuum pump) is developed. Lowering the pressure significantly reduces self-reversal effect, particularly for the Li I 610.4 nm line. This makes Li I 610.4 nm more suitable for analyzing high Lithium concentrations in MgLi alloys. Using standard samples, such as LA91 (8% Li) and LA141 (14% Li), the study successfully obtains Li I 610.4 nm spectra with proportional Li emission intensities. Even with a commercially affordable time-integrated charge-coupled device (CCD) detection system, the results indicate the efficacy of this approach for in situ Li analysis in MgLi alloys.