Journal articles: 'Forward detection systems'

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Kabakchiev, Hristo, Vera Behar, Ivan Garvanov, Dorina Kabakchieva, Avgust Kabakchiev, and Hermann Rohling. "FSR Systems for Detection of Air Objects Using Cosmic Radio Emissions." Sensors 21, no. 2 (January 11, 2021): 465. http://dx.doi.org/10.3390/s21020465.

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The paper analyses the possibility of Forward Scatter Radar (FSR) systems to detect airplanes using cosmic emission from pulsars and planets (pulsar, Sun, Moon). A suboptimal multichannel algorithm for joint detection and evaluation of the parameters of the forward scattering signal created by an airplane (duration and velocity) is proposed, with preliminary compensation of the powerful direct signal emitted by cosmic sources (pulsar, Sun and Moon). The expressions for calculation of the Signal-to-Noise Ratio (SNR) at the input of the detector and the compensator are obtained. The detection characteristics are also obtained, and the requirements for the suppression coefficient of the compensator are evaluated. A methodology for calculating the maximum distance for detecting an aircraft using a described algorithm is proposed. The obtained results show that due to the Forward Scatter (FS) effect, there is the theoretical possibility to detect airplanes at close ranges by FSRs, which use very weak signals from cosmic sources.

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Kabakchiev, Hristo, Vera Behar, Ivan Garvanov, Dorina Kabakchieva, Avgust Kabakchiev, and Hermann Rohling. "FSR Systems for Detection of Air Objects Using Cosmic Radio Emissions." Sensors 21, no. 2 (January 11, 2021): 465. http://dx.doi.org/10.3390/s21020465.

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The paper analyses the possibility of Forward Scatter Radar (FSR) systems to detect airplanes using cosmic emission from pulsars and planets (pulsar, Sun, Moon). A suboptimal multichannel algorithm for joint detection and evaluation of the parameters of the forward scattering signal created by an airplane (duration and velocity) is proposed, with preliminary compensation of the powerful direct signal emitted by cosmic sources (pulsar, Sun and Moon). The expressions for calculation of the Signal-to-Noise Ratio (SNR) at the input of the detector and the compensator are obtained. The detection characteristics are also obtained, and the requirements for the suppression coefficient of the compensator are evaluated. A methodology for calculating the maximum distance for detecting an aircraft using a described algorithm is proposed. The obtained results show that due to the Forward Scatter (FS) effect, there is the theoretical possibility to detect airplanes at close ranges by FSRs, which use very weak signals from cosmic sources.

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Ray, Loye Lynn, and Henry Felch. "Improving Performance and Convergence Rates in Multi-Layer Feed Forward Neural Network Intrusion Detection Systems." International Journal of Strategic Information Technology and Applications 5, no. 3 (July 2014): 24–36. http://dx.doi.org/10.4018/ijsita.2014070102.

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Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and scholarly journals. A key word search was used to compare and contrast the literature to find strengths, weaknesses and gaps. The significance of the research found that further work is needed to improve the performance and convergence rates of MLFFNN IDSs. This literature review contributes to the area of intrusion detection by looking at the effects of architecture, algorithms, and input data on the performance and convergence rates of MLFFNN IDSs.

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de Almeida, A. L. F., C. A. R. Fernandes, and Daniel Benevides da Costa. "Multiuser Detection for Uplink DS-CDMA Amplify-and-Forward Relaying Systems." IEEE Signal Processing Letters 20, no. 7 (July 2013): 697–700. http://dx.doi.org/10.1109/lsp.2013.2260738.

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Mao, Minghe, Ning Cao, Yunfei Chen, and Haobing Chu. "Novel noncoherent detection for multi-hop amplify-and-forward relaying systems." International Journal of Communication Systems 29, no. 7 (December 18, 2015): 1293–304. http://dx.doi.org/10.1002/dac.3099.

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Hindriks, Rikkert. "Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling." PLOS ONE 15, no. 12 (December 11, 2020): e0242715. http://dx.doi.org/10.1371/journal.pone.0242715.

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Measurements on physical systems result from the systems’ activity being converted into sensor measurements by a forward model. In a number of cases, inversion of the forward model is extremely sensitive to perturbations such as sensor noise or numerical errors in the forward model. Regularization is then required, which introduces bias in the reconstruction of the systems’ activity. One domain in which this is particularly problematic is the reconstruction of interactions in spatially-extended complex systems such as the human brain. Brain interactions can be reconstructed from non-invasive measurements such as electroencephalography (EEG) or magnetoencephalography (MEG), whose forward models are linear and instantaneous, but have large null-spaces and high condition numbers. This leads to incomplete unmixing of the forward models and hence to spurious interactions. This motivated the development of interaction measures that are exclusively sensitive to lagged, i.e. delayed interactions. The drawback of such measures is that they only detect interactions that have sufficiently large lags and this introduces bias in reconstructed brain networks. We introduce three estimators for linear interactions in spatially-extended systems that are uniformly sensitive to all lags. We derive some basic properties of and relationships between the estimators and evaluate their performance using numerical simulations from a simple benchmark model.

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Schreiner, K. "Landmine detection research pushes forward, despite challenges." IEEE Intelligent Systems 17, no. 2 (2002): 4–7. http://dx.doi.org/10.1109/5254.995459.

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Lu, You Li, and Jun Luo. "Imbalanced Data Detection Kernel Method in Closed Systems." Advanced Materials Research 756-759 (September 2013): 3652–58. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3652.

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Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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Veselov, V. V., A. M. Nechipai, E. A. Poltoryhina, and A. V. Vasilchenko. "FIRST EXPERIENCE IN FULL-SPECTRUM COLONOSCOPY." Koloproktologia, no. 2 (June 30, 2017): 36–46. http://dx.doi.org/10.33878/2073-7556-2017-0-2-36-46.

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Colonoscopy with a forward-viewing camera leaves regions that are not visualized in detail. Thus development of video-enoscopy systems with wide angle of view is needed. Full-spectrum colonoscopes providing image of Ultra HD 4K quality are now available in Russia. MATERIALS AND METHODS. Seventy patents were assessed with a full-spectrum colonoscope. In 51 (72,8°%) of them the procedure was performed also for physician's training purposes. Fifteen (21,4%) patients underwent simultaneous full-spectrum and forward-viewing colonoscopies, while in 4 (5,7%) full-spectrum endoscope was used to visualize lesions that were non-assessable with traditional equipment. RESULTS. Applying Jull-spectrum colonoscopy for diagnosis resulted in detecting 170 polyps in 51 patients (polyp detection rate was 47,1%). Simultaneous use of full-spectrum colonoscope after forward-viewing equipment led to 9 additional polyps detection in one patient and 23 additional polyps in another one. In 7 patents full-spectrum colonoscopy allowed detection of polyps that were not found via forward-viewing equipment. CONCLUSION. During full-spectrum colonoscopy inner colonic surface can be visualized with an angle of view of 330° which is twice more than video-capturing area ofa standard forward-viewing endoscope. The equipment allows to significantly increase adenoma detection rate.

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He Yucheng, Qiao Ying, and Zhou Lin. "Generalized maximum likelihood noncoherent block detection for decode-and-forward relay systems." China Communications 11, no. 4 (April 2014): 163–71. http://dx.doi.org/10.1109/cc.2014.6827578.

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Shi, Shuping, Stan Hurn, and Peter C. B. Phillips. "Causal Change Detection in Possibly Integrated Systems: Revisiting the Money–Income Relationship*." Journal of Financial Econometrics 18, no. 1 (March 6, 2019): 158–80. http://dx.doi.org/10.1093/jjfinec/nbz004.

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Abstract This paper re-examines changes in the causal link between money and income in the United States over the past half century (1959–2014). Three methods for the data-driven discovery of change points in causal relationships are proposed, all of which can be implemented without prior detrending of the data. These methods are a forward recursive algorithm, a rolling window algorithm, and a recursive evolving algorithm all of which utilize subsample tests of Granger causality within a lag-augmented vector autoregressive framework. The limit distributions for these subsample Wald tests are provided. Bootstrap methods are developed to control family-wise size in the implementation of the recursive testing algorithms. The results from a suite of simulation experiments suggest that the recursive evolving window algorithm provides the most reliable results, followed by the rolling window method. The forward expanding window procedure is shown to have the worst performance. Both the rolling window and recursive evolving approaches find evidence of Granger causality running from money to income during the Volcker period in the 1980s. The forward algorithm does not find any evidence of causality over the entire sample period.

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Bellini, Tiziano. "Forward search outlier detection in data envelopment analysis." European Journal of Operational Research 216, no. 1 (January 2012): 200–207. http://dx.doi.org/10.1016/j.ejor.2011.07.023.

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Park, Jisoo, Jingdao Chen, Yong K. Cho, Dae Y. Kang, and Byung J. Son. "CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems." Sensors 20, no. 1 (December 19, 2019): 34. http://dx.doi.org/10.3390/s20010034.

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Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.

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He, Xiao, Yanyan Hu, and Kaixiang Peng. "Intermittent Fault Detection for Uncertain Networked Systems." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/282168.

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This paper investigates intermittent fault detection problem for a class of networked systems with multiple state delays and unknown input. Polytopic-type parameter uncertainty in the state-space model matrices is considered. A novel measurement model is employed to account for both the random measurement delays and the stochastic data missing (package dropout) phenomenon, which are typically resulted from the limited capacity of the communication networks. We aim to design an uncertainty-dependent fault detection filter such that, for all unknown input, all possible parameter uncertainties, and all incomplete measurements, the error between residual and weighted fault is made as small as possible. By converting the addressed robust fault detection problem into an alternative robustH∞filtering problem of a certain Markovian jumping system (MJS), a sufficient condition for the existence of the desired robust fault detection filter is derived. A residual evaluation within an incremental form is brought forward to make the whole method suitable for intermittent fault detection. A numerical example is utilized to demonstrate the effectiveness of the proposed approach.

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de Beer, Tjipke, Gerard Ph Hoornweg, Arjan Termaten, Udo A. Th Brinkman, Nel H. Velthorst, and Cees Gooijer. "Forward-scattering degenerate four-wave mixing for sensitive absorption detection in microseparation systems." Journal of Chromatography A 811, no. 1-2 (June 1998): 35–45. http://dx.doi.org/10.1016/s0021-9673(98)00258-1.

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Hong, Sunghoon, and Daejin Park. "Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems." Sensors 22, no. 8 (April 14, 2022): 2998. http://dx.doi.org/10.3390/s22082998.

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Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle’s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is.

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Biyik, Can, Zaheer Allam, Gabriele Pieri, Davide Moroni, Muftah O’Fraifer, Eoin O’Connell, Stephan Olariu, and Muhammad Khalid. "Smart Parking Systems: Reviewing the Literature, Architecture and Ways Forward." Smart Cities 4, no. 2 (April 28, 2021): 623–42. http://dx.doi.org/10.3390/smartcities4020032.

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The Internet of Things (IoT) has come of age, and complex solutions can now be implemented seamlessly within urban governance and management frameworks and processes. For cities, growing rates of car ownership are rendering parking availability a challenge and lowering the quality of life through increased carbon emissions. The development of smart parking solutions is thus necessary to reduce the time spent looking for parking and to reduce greenhouse gas emissions. The principal role of this research paper is to analyze smart parking solutions from a technical perspective, underlining the systems and sensors that are available, as documented in the literature. The review seeks to provide comprehensive insights into the building of smart parking solutions. A holistic survey of the current state of smart parking systems should incorporate the classification of such systems as big vehicular detection technologies. Finally, communication modules are presented with clarity.

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Reeves. "Attention and Signal Detection." Information 10, no. 8 (August 7, 2019): 254. http://dx.doi.org/10.3390/info10080254.

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In this paper, I first review signal detection theory (SDT) approaches to perception, and then discuss why it is thought that SDT theory implies that increasing attention improves performance. Our experiments have shown, however, that this is not necessarily true. Subjects had either focused attention on two of four possible locations in the visual field, or diffused attention to all four locations. The stimuli (offset letters), locations, conditions, and tasks were all known in advance, responses were forced-choice, subjects were properly instructed and motivated, and instructions were always valid—conditions which should optimize signal detection. Relative to diffusing attention, focusing attention indeed benefitted discrimination of forward from backward pointing Es. However, focusing made it harder to identify a randomly chosen one of 20 letters. That focusing can either aid or disrupt performance, even when cues are valid and conditions are idealized, is surprising, but it can also be explained by SDT, as shown here. These results warn the experimental researcher not to confuse focusing attention with enhancing performance, and warn the modeler not to assume that SDT is unequivocal.

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Ding, Lianhong, Bin Sun, and Peng Shi. "Chinese Microblog Topic Detection through POS-Based Semantic Expansion." Information 9, no. 8 (August 10, 2018): 203. http://dx.doi.org/10.3390/info9080203.

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A microblog is a new type of social media for information publishing, acquiring, and spreading. Finding the significant topics of a microblog is necessary for popularity tracing and public opinion following. This paper puts forward a method to detect topics from Chinese microblogs. Since traditional methods showed low performance on a short text from a microblog, we put forward a topic detection method based on the semantic description of the microblog post. The semantic expansion of the post supplies more information and clues for topic detection. First, semantic features are extracted from a microblog post. Second, the semantic features are expanded according to a thesaurus. Here TongYiCi CiLin is used as the lexical resource to find words with the same meaning. To overcome the polysemy problem, several semantic expansion strategies based on part-of-speech are introduced and compared. Third, an approach to detect topics based on semantic descriptions and an improved incremental clustering algorithm is introduced. A dataset from Sina Weibo is employed to evaluate our method. Experimental results show that our method can bring about better results both for post clustering and topic detection in Chinese microblogs. We also found that the semantic expansion of nouns is far more efficient than for other parts of speech. The potential mechanism of the phenomenon is also analyzed and discussed.

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Sinha, Rohit, Rittika Sur, Ruchi Sharma, and Avinash K. Shrivastava. "Anomaly Detection Using System Logs." International Journal of Information Security and Privacy 16, no. 1 (January 2022): 1–15. http://dx.doi.org/10.4018/ijisp.285584.

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Anomaly detection is a very important step in building a secure and trustworthy system. Manually it is daunting to analyze and detect failures and anomalies. In this paper, we proposed an approach that leverages the pattern matching capabilities of Convolution Neural Network (CNN) for anomaly detection in system logs. Features from log files are extracted using a windowing technique. Based on this feature, a one-dimensional image (1×n dimension) is generated where the pixel values of an image correlate with the features of the logs. On these images, the 1D Convolution operation is applied followed by max pooling. Followed by Convolution layers, a multi-layer feed-forward neural network is used as a classifier that learns to classify the logs as normal or abnormal from the representation created by the convolution layers. The model learns the variation in log pattern for normal and abnormal behavior. The proposed approach achieved improved accuracy compared to existing approaches for anomaly detection in Hadoop Distributed File System (HDFS) logs.

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Park, Han G., and Michail Zak. "Gray-Box Approach for Fault Detection of Dynamical Systems." Journal of Dynamic Systems, Measurement, and Control 125, no. 3 (September 1, 2003): 451–54. http://dx.doi.org/10.1115/1.1589032.

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We present a fault detection method called the gray-box. The term “gray-box” refers to the approach wherein a deterministic model of system, i.e., “white box,” is used to filter the data and generate a residual, while a stochastic model, i.e., “black-box” is used to describe the residual. The residual is described by a three-tier stochastic model. An auto-regressive process, and a time-delay feed-forward neural network describe the linear and nonlinear components of the residual, respectively. The last component, the noise, is characterized by its moments. Faults are detected by monitoring the parameters of the auto-regressive model, the weights of the neural network, and the moments of noise. This method is demonstrated on a simulated system of a gas turbine with time delay feedback actuator.

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Băroiu, Alexandru-Costin, and Ștefan Trăușan-Matu. "Automatic Sarcasm Detection: Systematic Literature Review." Information 13, no. 8 (August 22, 2022): 399. http://dx.doi.org/10.3390/info13080399.

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Sarcasm is an integral part of human language and culture. Naturally, it has garnered great interest from researchers from varied fields of study, including Artificial Intelligence, especially Natural Language Processing. Automatic sarcasm detection has become an increasingly popular topic in the past decade. The research conducted in this paper presents, through a systematic literature review, the evolution of the automatic sarcasm detection task from its inception in 2010 to the present day. No such work has been conducted thus far and it is essential to establish the progress that researchers have made when tackling this task and, moving forward, what the trends are. This study finds that multi-modal approaches and transformer-based architectures have become increasingly popular in recent years. Additionally, this paper presents a critique of the work carried out so far and proposes future directions of research in the field.

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Pamungkas, Tri Wisnu, Resi Taufan, Petrus Damianus Batlayeri, Gabriel Vangeran Saragih, and Tri Retnasari. "DIAGNOSIS DETECTION OF ACUTE RESPIRATOR INFECTION WITH FORWARD CHAINING METHOD." Jurnal Techno Nusa Mandiri 18, no. 1 (March 15, 2021): 73–80. http://dx.doi.org/10.33480/techno.v18i1.2225.

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Many acute respiratory infections or ARI are caused by viruses that attack the nose, trachea (breathing tube), or the lungs. It can be said that ARI is caused by inflammation that disrupts a person's breathing process. If not treated quickly, ARI can spread to all respiratory systems and prevent the body from getting proper oxygen, moreover it can cause the loss of a person's life. This research aims to diagnose ARI as an early step in practicing artificial intelligence in medicine, designing and apply an expert system that can diagnose ARI. The procedure used in this study uses forward chaining with tracking that begins with input data, and then creates a diagnosis or solution. The expert system used to diagnose acute respiratory inflammation uses the Forward chaining procedure with a data-driven approach, in this approach tracking starts from input data, and then seeks to draw conclusions, so that it can be used. diagnose the type of disease associated with the ARD disease experienced by showing the existing signs.

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Goel, Dhanesh, Vemuri Sai Krishna, and Manav Bhatnagar. "Selection relaying in decode-and-forward multi-hop cognitive radio systems using energy detection." IET Communications 10, no. 7 (May 5, 2016): 753–60. http://dx.doi.org/10.1049/iet-com.2015.0209.

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Ramasamy, Mathiyalagan, and Pamela Vinitha Eric. "A tree growth based forward feature selection algorithm for intrusion detection system on convolutional neural network." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 472–82. http://dx.doi.org/10.11591/eei.v12i1.4015.

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With the rapid advancement of networking technologies, security system has become increasingly important to academics from several sectors. Intrusion detection (ID) provides a valuable protection by reducing the human resources required to keep an eye on intruders, improving the efficiency of detecting the various attacks in networks. Machine learning and deep learning are two key areas that have recently received a lot of attention, with a focus on improving the precision of detection classifiers. Using defense anvance research project agency (DARPA”98) datasets, a number of academics and research have developed intrusion detection systems. This paper discusses various approaches developed by different researchers, including scale-hybrid-IDS-AlertNet (SHIA), forward feature selection algorithm (FFSA), modified- mutual information feature selection (MMIFS), deep neural network (DNN), and the holes that remain to be filled, highlighting areas where these procedures can be improved, also are addressed and the proposed approach improved deep convolutional neural network (IDCNN) is compared with existing approach.

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Krishna, Komanduri, and Battula Prakash. "Intrusion Detection System Employing Multi-level Feed Forward Neural Network along with Firefly Optimization (FMLF2N2)." Ingénierie des systèmes d information 24, no. 2 (July 10, 2019): 139–45. http://dx.doi.org/10.18280/isi.240202.

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Relkin, Evan M., and Robert L. Smith. "Forward masking of the compound action potential: Thresholds for the detection of the N1 peak." Hearing Research 53, no. 1 (May 1991): 131–40. http://dx.doi.org/10.1016/0378-5955(91)90220-4.

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Yuan, Jin Song, and Yi Wang. "The Development of Intrusion Detection System Based on Improved BP Neural Network." Advanced Materials Research 718-720 (July 2013): 1973–79. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1973.

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BP neural network is a multilayer feed-forward neural network, it achieved from input to output arbitrary nonlinear mapping, and weights are adjusted by using the back propagation learning algorithm. Intrusion detection systems using the learning ability of neural network to extract the network data profile, and it also can use the neural network has the ability of self-learning and parallel processing ability, through the construction of intelligent neural network classifier to identify abnormal, so as to achieve the purpose of detecting intrusion behavior. The paper proposes the development of intrusion detection system based on improved BP neural network. Experimental results show that the proposed algorithm has high efficiency.

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Chen, Zhuo, Jonathan J. Chen, and Rong Fan. "Single-Cell Protein Secretion Detection and Profiling." Annual Review of Analytical Chemistry 12, no. 1 (June 12, 2019): 431–49. http://dx.doi.org/10.1146/annurev-anchem-061318-115055.

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Secreted proteins play important roles in mediating various biological processes such as cell–cell communication, differentiation, migration, and homeostasis at the population or tissue level. Here, we review bioanalytical technologies and devices for detecting protein secretions from single cells. We begin by discussing conventional approaches followed by detailing the latest advances in microengineered systems for detecting single-cell protein secretions with an emphasis on multiplex measurement. These platforms include droplet microfluidics, micro-/nanowell-based assays, and microchamber-based assays, among which the advantages and limitations are compared. Microscale systems also enable the tracking of protein secretion dynamics in single cells, further empowering the study of the cell–cell communication network. Looking forward, we discuss the remaining challenges and future opportunities that will transform basic research of cellular secretion functions at the systems level and the clinical applications for immune monitoring and cancer treatment.

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Zhao, Guo, and Xue Liang Huang. "Composite Islanding Detection Method of Grid-Connected Photovoltaic Generation System." Applied Mechanics and Materials 339 (July 2013): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amm.339.574.

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Islanding detection is an important issue for grid-connected photovoltaic (PV) generation systems. The paper puts forward a new composite islanding detection method combining active current disturbance and reactive current disturbance. This islanding detection method for three-phase inverter is simulated in the Matlab/Simulink environment. Simulation results show that, when the inverter output and the load power are balanced, the new islanding detection method can detect islanding quickly with little influence on the power quality.

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Voutsinas, Stylianos, Dimitrios Karolidis, Ioannis Voyiatzis, and Maria Samarakou. "Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems." Energy Reports 8 (November 2022): 33–42. http://dx.doi.org/10.1016/j.egyr.2022.06.107.

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Sha, Xiao Yan. "Research on Fuzzy Control of Mine Ventilation Based on Embedded Systems." Applied Mechanics and Materials 686 (October 2014): 126–31. http://dx.doi.org/10.4028/www.scientific.net/amm.686.126.

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Taking embedded processor as the core control unit, the paper designs the fan monitoring system software and hardware to achieve the fan working condition detection and real-time control. For the control algorithm, the paper analyzes the fuzzy control system theory and composition, and then combined with tunnel ventilation particularity, introduce feed-forward model to predict the incremental acquisition of pollutants to reduce lag, combined with the system feedback value and the set value, by calculate of two independent computing fuzzy controller, and ultimately determine the number of units increase or decrease in the tunnel jet fans start and stop. Through simulation analysis, the introduction of a feed-forward signal, it can more effectively improve the capability of the system impact of interference.

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Alves-Pinto, Ana, Sylvie Baudoux, Alan R. Palmer, and Christian J. Sumner. "Forward Masking Estimated by Signal Detection Theory Analysis of Neuronal Responses in Primary Auditory Cortex." Journal of the Association for Research in Otolaryngology 11, no. 3 (April 6, 2010): 477–94. http://dx.doi.org/10.1007/s10162-010-0215-6.

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Georgeson, Mark A. "Spatial phase dependence and the role of motion detection in monocular and dichoptic forward masking." Vision Research 28, no. 11 (January 1988): 1193–205. http://dx.doi.org/10.1016/0042-6989(88)90036-3.

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Jin, Haiyan, Yuxin Wu, Guodong Xu, and Zhilu Wu. "Research on an Urban Low-Altitude Target Detection Method Based on Image Classification." Electronics 11, no. 4 (February 19, 2022): 657. http://dx.doi.org/10.3390/electronics11040657.

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With the expansion of the civil UAV (Unmanned Aerial Vehicle) market, UAVs are also increasingly being used in illegal activities such as espionage and snooping on privacy. Therefore, how to effectively control the activities of UAVs in cities has become an urgent problem to be solved. Considering the urban background and the radar performance of communication signals, a low-altitude target detection scheme based on 5G base stations is proposed in this paper. A 5G signal is used as the external radiation source, the method of transceiver separation is adopted, and the forward-scattered waves are used to complete the detection of UAV. This paper mainly analyzes the principle of forward scattering detection in an urban environment, where the forward-scattered wave of a target is stronger than the backward-reflected wave and contains both height difference and midline height information on the target. Based on the above theory, this paper proposes a forward-scattered wave recognition algorithm based on YOLOv3-FCWImageNet, which transforms the forward-scattered wave recognition problem into a target detection problem and accomplishes the recognition of forward-scattered waves by using the excellent performance of algorithms in the field of image recognition. Simulation results show that FCWImageNet can distinguish two different low-altitude targets effectively, and realize the monitoring and classification of UAVs.

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Sun, Yu Tao. "Design and Research on Intrusion Detection System in the Computer Network Security." Applied Mechanics and Materials 416-417 (September 2013): 1418–22. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1418.

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This paper first discusses the information security and network security, security threat, hacker intrusion process, system and network security vulnerabilities, and then introduces the status of intrusion detection system. By the comparison of two kinds of intrusion detection systems, the article puts forward the detection system based on the combination of the soil and the intrusion of network intrusion detection technology. Combined with the actual project development, this article focuses on the key technology design idea and the realization of the intrusion detection system in network security.

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Cui, PengHui, Peng Liu, and Xinghua Qian. "Vehicle position prediction systems and performance equalization." Journal of Physics: Conference Series 2347, no. 1 (September 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2347/1/012016.

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Abstract This article will first solve the problem of beam prediction for the link between the vehicle and the communication awareness integrated system. The dual-function communication perception integrated signal emitted by the roadside module (RSM) is used for beam tracking. At the same time, the position parameters of the vehicle are estimated through the reflected echo signal. Based on the above estimation, we put forward a method to forecast the location of the cars. After adding the equalization parameter, it determines the weight of the perception and communication performance in the communication perception integrated system. By equalizing the communication signal power and the radar detection power, the system can maintain high detection accuracy while ensuring better communication power. Finally, the effectiveness of the method is verified by simulation experiments, and the experimental results show that this method is better than the traditional scheme in terms of achievable communication rate.

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Li, Wenhui, Peixun Liu, Ying Wang, and Hongyin Ni. "Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/701058.

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Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

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Liang, Xiaoqiang, Da Hu, Yongsuo Li, Yunyi Zhang, and Xian Yang. "Application of GPR Underground Pipeline Detection Technology in Urban Complex Geological Environments." Geofluids 2022 (May 20, 2022): 1–9. http://dx.doi.org/10.1155/2022/7465919.

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In the process of the continuous construction of underground pipelines, underground pipe network systems have become increasingly complex, which puts forward higher requirements for normal operation and maintenance. To address different kinds of complex conditions, this experiment in the present paper takes ground penetrating radar as the research basis and uses a self-correction and screening algorithm to innovatively detect underground pipelines. The results show that urban underground pipeline detection technology based on ground penetrating radar (GPR) can obtain a highly reliable number of pipelines and track predefined pipelines when detecting different numbers of verification pipelines. When detecting underground pipelines in different sections, the vertical and horizontal errors are no more than 0.199 m and 0.248 m, respectively, which means that the detection technology of urban underground pipelines based on GPR has high detection accuracy and can be performed on high-level detection tasks under various complex conditions. This research applies bottom detection radar to urban underground pipeline detection technology under complex conditions for the first time, innovatively uses the action mechanism of bottom detection radar, integrates its high precision and high efficiency into underground pipeline detection technology, and ensures the effectiveness of the detection work.

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Ummadisetti, Ganesh Naidu, R. Thiruvengatanadhan, Satyala Narayana, and P. Dhanalakshmi. "Character level vehicle license detection using multi layered feed forward back propagation neural network." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 293–302. http://dx.doi.org/10.11591/eei.v12i1.4010.

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Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

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Chimeleze, Collins, Norziana Jamil, Roslan Ismail, Kwok-Yan Lam, Je Sen Teh, Joshua Samual, and Chidiebere Akachukwu Okeke. "BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning." Security and Communication Networks 2022 (October 11, 2022): 1–24. http://dx.doi.org/10.1155/2022/5339926.

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Malware detection refers to the process of detecting the presence of malware on a host system, or that of determining whether a specific program is malicious or benign. Machine learning-based solutions first gather information from applications and then use machine learning algorithms to develop a classifier that can distinguish between malicious and benign applications. Researchers and practitioners have long paid close attention to the issue. Most previous work has addressed the differences in feature importance or the computation of feature weights, which is unrelated to the classification model used, and therefore, the implementation of a selection approach with limited feature hiccups, and increases the execution time and memory usage. BFEDroid is a machine learning detection strategy that combines backward, forward, and exhaustive subset selection. This proposed malware detection technique can be updated by retraining new applications with true labels. It has higher accuracy (99%), lower memory consumption (1680), and a shorter execution time (1.264SI) than current malware detection methods that use feature selection.

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Kim, Tae-Kyoung. "Cooperative DF Protocol for MIMO Systems Using One-Bit ADCs." Sensors 22, no. 20 (October 15, 2022): 7843. http://dx.doi.org/10.3390/s22207843.

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This study considers a detection scheme for cooperative multi-input–multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs) in a decode-and-forward (DF) relay protocol. The use of one-bit ADCs is a promising technique for reducing the power consumption, which is necessary for supporting future wireless systems comprising a large number of antennas. However, the use of a large number of antennas remains still limited to mobile devices owing to their size. Cooperative communication using a DF relay can resolve this limitation; however, detection errors at the relay make it difficult to employ cooperative communication directly. This difficulty is more severe in a MIMO system using one-bit ADCs due to its nonlinear nature. To efficiently address the difficulty, this paper proposes a detection scheme that mitigates the error propagation effect. The upper bound of the pairwise error probability (PEP) of one-bit ADCs is first derived in a weighted Hamming distance form. Then, using the derived PEP, the proposed detection for the DF relay protocol is derived as a single weighted Hamming distance. Finally, the complexity of the proposed detection is analyzed in terms of real multiplications. The simulation results show that the proposed detection method efficiently mitigates the error propagation effect but has a relatively low level of complexity when compared to conventional detection methods.

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He, Matthias Y., Eric B. Ford, and Darin Ragozzine. "Architectures of exoplanetary systems – I. A clustered forward model for exoplanetary systems around Kepler’s FGK stars." Monthly Notices of the Royal Astronomical Society 490, no. 4 (November 5, 2019): 4575–605. http://dx.doi.org/10.1093/mnras/stz2869.

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ABSTRACT Observations of exoplanetary systems provide clues about the intrinsic distribution of planetary systems, their architectures, and how they formed. We develop a forward modelling framework for generating populations of planetary systems and ‘observed’ catalogues by simulating the Kepler detection pipeline (SysSim). We compare our simulated catalogues to the Kepler DR25 catalogue of planet candidates, updated to include revised stellar radii from Gaia DR2. We constrain our models based on the observed 1D marginal distributions of orbital periods, period ratios, transit depths, transit depth ratios, transit durations, transit duration ratios, and transit multiplicities. Models assuming planets with independent periods and sizes do not adequately account for the properties of the multiplanet systems. Instead, a clustered point process model for exoplanet periods and sizes provides a significantly better description of the Kepler population, particularly the observed multiplicity and period ratio distributions. We find that $0.56^{+0.18}_{-0.15}$ of FGK stars have at least one planet larger than 0.5R⊕ between 3 and 300 d. Most of these planetary systems ($\sim 98{{\ \rm per\ cent}}$) consist of one or two clusters with a median of three planets per cluster. We find that the Kepler dichotomy is evidence for a population of highly inclined planetary systems and is unlikely to be solely due to a population of intrinsically single planet systems. We provide a large ensemble of simulated physical and observed catalogues of planetary systems from our models, as well as publicly available code for generating similar catalogues given user-defined parameters.

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Braers, Sasskia, and Rufin VanRullen. "Visual target detection in temporal white-noise: A "universal" forward model using oscillatory impulse response functions." Journal of Vision 16, no. 12 (September 1, 2016): 1222. http://dx.doi.org/10.1167/16.12.1222.

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García-Fernández, María, Guillermo Álvarez-Narciandi, Yuri Álvarez López, and Fernando Las-Heras Andrés. "Analysis and Validation of a Hybrid Forward-Looking Down-Looking Ground Penetrating Radar Architecture." Remote Sensing 13, no. 6 (March 22, 2021): 1206. http://dx.doi.org/10.3390/rs13061206.

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Ground Penetrating Radar (GPR) has proved to be a successful technique for the detection of landmines and Improvised Explosive Devices (IEDs) buried in the ground. In the last years, novel architectures for safe and fast detection, such as those based on GPR systems onboard Unmanned Aerial Vehicles (UAVs), have been proposed. Furthermore, improvements in GPR hardware and signal processing techniques have resulted in a more efficient detection. This contribution presents an experimental validation of a hybrid Forward-Looking–Down-Looking GPR architecture. The main goal of this architecture is to combine advantages of both GPR architectures: reduction of clutter coming from the ground surface in the case of Forward-Looking GPR (FLGPR), and greater dynamic range in the case of Down-Looking GPR (DLGPR). Compact radar modules working in the lower SHF frequency band have been used for the validation of the hybrid architecture, which involved realistic targets.

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Masjedi, Maryam, Ali Mohammad Doost-Hoseini, Mohammad Mahdi Naghsh, and Saeed Gazor. "Partially Blind Joint Channel Estimation and Symbol Detection in Amplify-and-Forward Two-Way Relay Systems." IEEE Transactions on Communications 66, no. 12 (December 2018): 5966–75. http://dx.doi.org/10.1109/tcomm.2018.2864281.

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Wang, Jiaxing, Mugen Peng, Yaqiong Liu, Xiqing Liu, and Mahmoud Daneshmand. "Performance Analysis of Signal Detection for Amplify-and-Forward Relay in Diffusion-Based Molecular Communication Systems." IEEE Internet of Things Journal 7, no. 2 (February 2020): 1401–12. http://dx.doi.org/10.1109/jiot.2019.2955114.

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Yang, Fan, Zhiming He, Shisheng Guo, Yuanhua Fu, Liang Li, Junfeng Lu, and Kui Jiang. "Non-Contact Driver Respiration Rate Detection Technology Based on Suppression of Multipath Interference with Directional Antenna." Information 11, no. 4 (April 4, 2020): 192. http://dx.doi.org/10.3390/info11040192.

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Non-contact driver respiration rate detection is a challenging problem in the Internet of Vehicles, because the automobile environment is much narrower, and thus the multipath effect is greater. To overcome these challenges, a 2.4 GHz continuous wave forward-scattering radar respiratory detection system is proposed based on the theory that the radar cross-section (RCS) of the human body changes with human breathing. We also analyze the impact of the multipath effect in the vehicle on the received radar signal and compare the output signal captured by a directional antenna with that captured by an omnidirectional antenna in the proposed system. In addition, the mean value of the received signal’s envelope is used to judge whether the driver’s posture is reasonable. Finally, compared with the existing contact respiratory detection system, the actual test results demonstrate the effectiveness of the proposed FSR system, and the driver respiration rates obtained by the proposed system are consistent with those obtained by the contact respiratory detection system.

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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.

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Matsenko, Svitlana, Oleksiy Borysenko, Sandis Spolitis, Aleksejs Udalcovs, Lilita Gegere, Aleksandr Krotov, Oskars Ozolins, and Vjaceslavs Bobrovs. "FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects." Entropy 24, no. 1 (January 13, 2022): 122. http://dx.doi.org/10.3390/e24010122.

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Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional error control in the design of CM systems with high-performance requirements remains urgent. As an additional control of CM systems, we propose to use indivisible error detection codes based on a positional number system. In this work, we evaluated the indivisible code using the average probability method (APM) for the binary symmetric channel (BSC), which has the simplicity, versatility and reliability of the estimate, which is close to reality. The APM allows for evaluation and compares indivisible codes according to parameters of correct transmission, and detectable and undetectable errors. Indivisible codes allow for the end-to-end (E2E) control of the transmission and processing of information in digital systems and design devices with a regular structure and high speed. This study researched a fractal decoder device for additional error control, implemented in field-programmable gate array (FPGA) software with FEC for short-reach optical interconnects with multilevel pulse amplitude (PAM-M) modulated with Gray code mapping. Indivisible codes with natural redundancy require far fewer hardware costs to develop and implement encoding and decoding devices with a sufficiently high error detection efficiency. We achieved a reduction in hardware costs for a fractal decoder by using the fractal property of the indivisible code from 10% to 30% for different n while receiving the reciprocal of the golden ratio.

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Journal articles on the topic 'Forward detection systems'

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