Narrow Results

Effective energy management and optimal utilization of assets are crucial for a nation’s progress and prosperity. However, as electricity travels from power plants to end users, it incurs two types of losses: Technical Losses (TL) and Non-Technical Losses (NTL). TLs arise from outdated or inefficient technologies, whereas NTLs result from abnormal electricity usage, including electricity theft, which is often conducted to reduce expenses. These losses pose significant challenges to maintaining grid reliability and result in reduced profits for utility operators. Although the implementation of Automatic Metering Infrastructure (AMI) has improved grid predictability, it has also created new vulnerabilities for NTLs through Cyber-Physical Theft Attacks (CPTA). Machine learning techniques have been applied to identify and mitigate CPTA; however, they often fail to capture comprehensive Energy Consumption Patterns (ECPs), limiting their effectiveness in detecting malicious activities. Generative Adversarial Networks (GANs) have shown remarkable success in various anomaly detection applications. In the context of electricity anomaly identification and diagnosis, GANs are particularly effective in modeling typical energy usage patterns and detecting deviations. Building on this foundation, this study introduces an Adaptive GAN (AGAN) model to detect and diagnose electricity anomalies. The AGAN dynamically optimizes key parameters, such as the number of hidden neurons, epochs and steps per epoch, using the Improved Honey Badger Algorithm (IHBA). This optimization enhances the model’s performance, particularly in terms of Detection Accuracy, Critical Success Index (CSI) and False Omission Rate (FOR). The proposed model demonstrates its capability to accurately identify electricity anomalies and has been rigorously evaluated against existing anomaly detection methods. Results highlight AGAN’s superior effectiveness and robustness, establishing it as a reliable solution for improving the reliability and efficiency of electricity grid systems.

In view of the current problem of various cracks in the old residential areas, and the fact that these cracks may cause safety risks to the building, it is particularly important to carry out real-time monitoring, so as to give early warning in advance and take Corresponding safety measures. In this paper, it is proposed to use steel rulers, feeler gauges and acoustic detectors to measure the length, width and depth of cracks and establish an intelligent and integrated big data monitoring platform to monitor cracks in old residential areas in real time and give early warning to ensure that the houses are in a safe state. Through the big data system, the platform solves the problems of lagging traditional manual inspection, inefficient resource allocation and extensive management and lays the foundation for subsequent housing construction safety identification and crack treatment.

The number of radio signals used in mining operations is increasing dramatically, and this growth is creating unprecedented challenges for mine management. To address this issue, this paper proposes a deep learning-based RTL2032U+R820T radio signal analysis algorithm to make radio signal analysis in mines more intelligent. First, software-defined radio technology is employed to capture electromagnetic wave signals, convert analog signals into digital form and transmit them to the data analysis platform for processing. Second, the time window mode is used to divide the signal in the frequency domain into blocks, and feature extraction is performed on the processed electromagnetic wave signal based on energy-related features. Finally, the deep learning algorithm is used to further extract the serial signal feature vector to identify the radio signal. The article samples and analyzes 10 types of telecommunications equipment in real mines. The results show that this algorithm can effectively identify radio signals in mines, achieving an accuracy rate of 97.08%. This demonstrates that the proposed algorithm can significantly improve mine working efficiency, optimize equipment operating status and reduce the likelihood of failure.

Two mobile agents, starting at arbitrary, possibly different times from arbitrary nodes of an unknown network, have to meet at some node. Agents move in synchronous rounds: in each round an agent can either stay at the current node or move to one of its neighbors. Agents have different labels which are positive integers. Each agent knows its own label, but not the label of the other agent. In traditional formulations of the rendezvous problem, meeting is accomplished when the agents get to the same node in the same round. We want to achieve a more demanding goal, called rendezvous with detection: agents must become aware that the meeting is accomplished, simultaneously declare this and stop. This awareness depends on how an agent can communicate to the other agent its presence at a node. We use two variations of the arguably weakest model of communication, called the beeping model, introduced in [8]. In each round an agent can either listen or beep. In the local beeping model, an agent hears a beep in a round if it listens in this round and if the other agent is at the same node and beeps. In the global beeping model, an agent hears a loud beep in a round if it listens in this round and if the other agent is at the same node and beeps, and it hears a soft beep in a round if it listens in this round and if the other agent is at some other node and beeps.

We first present a deterministic algorithm of rendezvous with detection working, even for the local beeping model, in an arbitrary unknown network in time polynomial in the size of the network and in the length of the smaller label (i.e., in the logarithm of this label). However, in this algorithm, agents spend a lot of energy: the number of moves that an agent must make, is proportional to the time of rendezvous. It is thus natural to ask if bounded-energy agents, i.e., agents that can make at most c moves, for some integer c, can always achieve rendezvous with detection as well. This is impossible for some networks of unbounded size. Hence we rephrase the question: Can bounded-energy agents always achieve rendezvous with detection in bounded-size networks? We prove that the answer to this question is positive, even in the local beeping model but, perhaps surprisingly, this ability comes at a steep price of time: the meeting time of bounded-energy agents is exponentially larger than that of unrestricted agents. By contrast, we show an algorithm for rendezvous with detection in the global beeping model that works for bounded-energy agents (in bounded-size networks) as fast as for unrestricted agents.

A detection approach based on the principles of Fourier Transform Infrared Spectroscopy (FTIR) is presented for the trace level detection of toxic compounds in water. The main advantages of this approach are that it operates in heterogeneous aqueous environments, provides fast detection (< 10 min), and exhibits high sensitivity/selectivity to nonvolatile toxic materials with minimal false alarms. The key enablers to using FTIR for aqueous-based detection is the development of a selective and robust sampling protocol coupled to a miniaturized portable FTIR unit. The sampling approaches involve synthesizing and tailoring microporous, mesoporous, and nonporous metal oxide powders/films that are amenable for in situ FTIR measurements. In this paper we provide an overview of the material synthesis and surface modification strategies, and present results obtained using these materials for the low level detection of the organophosphate pesticide phosmet. Phosmet is used as a surrogate for the nerve agent VX.

Currently there exists a critical need within the military and homeland defense for highly sophisticated yet, small, lightweight portable sensors and detection systems for identifying and quantifying biological and biowarfare agents (BWA) in both liquid and aerosolized form. Our proposed BWA detection system is based upon Fourier Transform Infrared Spectroscopy (FTIR), where the main advantages of this approach are that it is reagentless, operates in heterogeneous aqueous environments, and provides fast detection and high sensitivity/selectivity to bacterial spores with minimal false alarms.

The key enabler to using FTIR for BWA detection is to develop selective and robust sampling protocols coupled to a miniaturized, portable FTIR unit. To that end, we have developed front-end liquid flow cells which incorporate electric field (E-Field) concentration methods for spores onto the surface of an Attenuated Total Reflection (ATR) IR crystal. IR spectra are presented which show collection and detection results with BG spores in water. The approaches we have developed take advantage of the fact that all spores are negatively charged in neutral pH solutions. Therefore, E-Field concentration of spores directly onto an ATR sampling element enables low level concentration measurements to be possible.

Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.

Threats associated with bioaerosol weapons have been around for several decades. However, with the recent political developments that changed the image and dynamics of the international order and security, the visibility and importance of these bioaerosol threats have considerably increased. Over the last few years, Defence Research and Development Canada has investigated the spectrometric LIDAR-based standoff bioaerosol detection technique to address this menace. This technique has the advantages of rapidly monitoring the atmosphere over wide areas without physical intrusions and reporting an approaching threat before it reaches sensitive sites. However, it has the disadvantages of providing a quality of information that degrades as a function of range and bioaerosol concentration. In order to determine the importance of these disadvantages, Canada initiated in 1999 the SINBAHD (Standoff Integrated Bioaerosol Active Hyperspectral Detection) project investigating the standoff detection and characterization of threatening biological clouds by Laser-Induced Fluorescence (LIF) and intensified range-gated spectrometric detection techniques. This article reports an overview of the different lessons learned with this program. Finally, the BioSense project, a Technology Demonstration Program aiming at the next generation of wide area standoff bioaerosol sensing, mapping, tracking and classifying systems, is introduced.

The detection method of microblog false information has been constructed based on block matching and fuzzy neural network to improve the detection accuracy of microblog false information effectively. With this method, we can calculate the rank distance and sample entropy of microblog data according to the evaluation word rank vector of microblog false information, carry out the block matching of false information in fuzzy data set and input the characteristic quantity of microblog false information extracted into the fuzzy neural network classifier for data classification and recognition. So that it has achieved the optimized detection of false information and improved the judgment ability of false information. Finally, the key factors that affect the algorithm are deeply studied through simulation experiments according to the real data of Sina microblog, and the performance state between the proposed algorithm and Fuzzy C-means and Spectral Analysis algorithms is compared and analyzed correspondingly. The results show that the algorithm has good adaptability.

The possibility of detecting neutrinos by coherent scattering on high Debye temperature monocrystals such as sapphire is presented and discussed. Preliminary estimations showed that 1 MeV neutrinos with a fluency density of 10¹² cm^-2 s^-1 could interact with a force of about 10^-6 dyn with a 100 g sapphire monocrystal. A torsion balance provided with 1 m length molybdenum wire and an optical autocollimator able to measure small rotation angles of about 0.1 arcsec could give positive results. The design of a high sensitivity torsion balance under construction provided with such a detector is presented and discussed.

The possibility of detecting solar neutrinos by coherent scattering in high-Debye temperature monocrystals such as sapphire is presented and discussed. Preliminary experimental results indicate that the solar neutrinos flux (0–430 keV) produces an observable torque of a high-sensitive torsion balance. Our experiments give a result for the diurnal force as predicted by Weber's theory of enhanced solar neutrinos coherent scattering.

We present the results of cumulant analysis for detection of random process using a Schottky diode with δ-doping. The statistical characteristics of the output process of the detector, based on a Schottky diode with δ-doping, are investigated. We discuss noninertial and inertial detection mode. It was shown that at a relatively large dispersion of the input noise a noninertial detection mode occurs.

To investigate the decoherence of Kondo singlet, we once again check a model, an Aharonov–Bohm interferometer with a quantum dot coupling to left–right electrodes, which is designed by Yacoby to measure phase-sensitive of a quantum dot. By employing the cluster expansion, the equations of motion of Green's functions are transformed into the corresponding equations of connected Green's functions, which contain the correlation of two conducting electrons. With the method, we show that the Kondo singlet is suppressed by phase-sensitive detection of Aharonov–Bohm interferometer. Our numerical results have provided a qualitative explanation with the anomalous features observed in an experiment by Avinun-Kalish et al. [Phys. Rev. Lett.92 (2004) 156801].

It is known that redox reaction can take place among the solutions of potassium ferrocyanide (K₄[Fe(CN)₆]), glucose (C₆H${}_{12}$O₆) and glucose oxidase (Glucose Oxidase, GOD). In this work, the method of electrochemical biosensor detection based on screen printed electrode was used to observe the redox reaction among these solutions. The relationship between redox reaction and parameters was studied by examining the effects of concentration and scanning speed of three solutions.

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image.

To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

Detection process of airborne targets may be thought simple because of the incompatible nature of aircraft, choppers, UAVs, and drones regarding clear sky background. When changes in the background are considered, brightness variation of the sky complicates the process. Changes in the shapes and types of clouds add another challenge to the process. Tracking process directly depends on the detection process and type of the data stream. The practical systems used for video detection and tracking of airborne targets are manual, and manual structures have some drawbacks compared to automatic structures. For video surveillance, guidance, regional security, and defense applications in dense environments, automatic detection and tracking process may be an obligation rather than preference. In this study, an automatic detection and tracking algorithm for video streams of airborne targets is proposed. A land-based moving camera captures the video data, and not only the flying objects but probably also the camera are in motion. Although the detection and tracking of moving objects via moving sensors is a relatively arduous task, this is the prevalent case in real-life scenarios. Video detection and tracking systems have one or more moving video sensors, while one or more flying air vehicles are in operation area. The proposed algorithm includes an image processing stage for detection and a tracking stage for initiation and continuation. An assessment study has been conducted for the actual video data and found that the proposed method yields successful results for detection, track formation, and continuation processes.

With the changing lifestyle, a large population suffers from a bone disease known as an osteoarthritis affecting the knee, spine, and hip. Therefore, timely detection and classification of the disease are necessary to minimize the loss, however, it is a time-consuming task and requires various tests and physicians’ in-depth analysis. Thus, an accurate automated technique, timely detection and classification are needed to cope with the aforementioned challenges. This study proposes a technique based on an efficient DenseNet that uses the knee image’ features to identify the Knee Osteoarthritis (KOA) and determine its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. We introduced the reweighted cross-entropy loss function which makes our proposed algorithm more robust as the training data is imbalanced. The dense connections of efficient DenseNet with regularization power help to reduce the overfitting during the training of small knee sample training sets. The proposed algorithm is an efficient approach that can identify the early symptoms of KOA and classify the severity level of the disease for better decision making by orthopedics. The algorithm is a pre-trained network that does not require a huge training set, therefore, the existing dataset i.e. Mendeley VI has been utilized for the training and testing. Additionally, cross-validation has been employed using the OAI dataset to assess the performance of the proposed model. The algorithm achieved 98.22% accuracy over the testing set and 98.08% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm is more consistent and capable of detecting and classifying the KOA disease than existing state of the art.

With the rapid growth and widespread use of low-end commercial unmanned aerial vehicles (UAVs), it is critical to develop an object detection system that works well with these devices. This paper designs an efficient and lightweight object detection model specifically designed for low-end UAVs. Through excellent information interaction and the refined use of some techniques, our model can obtain multi-scale features and better focus on the details of different parts. Extensive experiments show that our model still maintains comparable accuracy while it consumes fewer parameters and FLOPs. In addition, our model has been applied to the tracking system of low-end UAVs, greatly enhancing the tracking performance.

Mammographic screening programmes generate large numbers of highly variable, complex images, most of which are unequivocally normal. When present, abnormalities may be small or subtle. Two processes critical to the success of screening programmes are the perception of potential abnormalities and the subsequent analy-sis of each detected lesion to determine its clinical significance. The consequences of errors are costly, and in many screening centres, films are read by two radiologists in an attempt to reduce errors. The prime objective of our research is to improve the accuracy of the detection and analysis of breast lesions by providing radiologists with computer-aided digital image analysis tools. In this paper we focus on the detection and analysis of mammographic microcalcifications.

We describe a philosophy of research aimed at generating useful computer-based aids for radiologists. Firstly, it is necessary to accurately identify specific tasks which are difficult for the human observer. Having correctly identified a problem, appropriate computer vision methods must be developed and their performance evaluated. It is then important to determine effective ways of using such methods to aid radiologists, and it is essential to prove that the effect on radiologists’ performance is entirely beneficial.

We present results of experiments to determine factors affecting radiologists’ perception of microcalcifications, and to investigate the effects of attention-cueing on detection performance. Our results show that radiologists’ performance can be significantly improved with the use of prompts generated from automatically-detected microcalcification clusters.

We describe a new method for the delineation of mammographic abnormalities based on the analysis of multiple high quality X-ray projections of excised lesions. Biopsy specimens are secured inside a rigid tetrahedron, the edges of which provide a reference frame to which the locations of features can be related. A three-dimensional representation of an abnormality can be formed and rotated to resemble its appearance in the original mammogram.