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International Power Electronics Conference

作者： Thusitha Randima Wellawatta Sung-Jin Choi School of Electrical Engineering University of Ulsan Ulsan South Korea

Maximum power point tracking (MPPT) under partial shading condition (PSC) is a challenging research topic in the PV array system. As the shaded PV module makes different peak patterns on the power versus voltage curve and misguides the MPPT algorithm, various kinds of global MPP (GMPP) detecting algorithms have been studied. Generally, too frequent execution of the GMPP tracking algorithm reduces the achievable power of PV module due to time spent on the scanning process. Thus, the partial shading detection algorithm is essential for efficient utilization of solar energy source. Based on the theoretical investigation of the characteristic curve patterns under various partial shading conditions, this paper presents a new detection algorithm utilizing power level monitoring. While conventional methods only focus on fast shading patterns, the proposed algorithm always shows superb performance regardless of the partial shading patterns.

关键词： Novel Partial Shading detection algorithm Photovoltaic Panels

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Synthesis and Analysis of detection algorithm of Distributed Sources Against Background With Distributed Clutter

Synthesis and Analysis of Detection Algorithm of Distributed...

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International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering

作者： Leonid G.Dorosinskiy Nadezhda V. Budyldina Urals Technical Institute of Communication and Informatics Yekaterinburg Russia

ISBN: (纸本)9781509040704

The paper deals with an algorithm for detection of a broadband signal received from a multiple-unit source against the background with architecturally-like clutters that differ by location and additive space-uncorrelated noise. It is shown that the optimal detection algorithm represents the weight processing in the ?ray space? of the transformed vector of observed data and does not demand access of any large matrix. A maximum likelihood ratio is chosen as the optimality criterion. After determining the inverse of the correlation function, expression for the optimal weight function is obtained. The design ratios for determination of detection characteristics under the Gaussian statistics of signals and clutters are given. The results confirm the property of the suggested algorithm: at low signal/noise ratios, efficiency of point targets detection is better, and at the large ratios the distributed targets are better detected.

关键词： detection algorithm distributed clutter

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Robust automatic crater detection at all latitudes on Mars with deep-learning

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PLANETARY AND SPACE SCIENCE 2025年 260卷

作者： Martinez, L. Andrieu, F. Schmidt, F. Talbot, H. Bentley, M. S. Univ Paris Saclay CNRS GEOPS F-91405 Orsay France Inst Univ France IUF Paris France CentraleSupelec Paris Saclay Univ Inria Gif Sur Yvette France European Space Astron Ctr Camino Bajo CastilloUrb Villafranca CastilloVill Madrid 28692 Spain

Understanding the distribution and characteristics of impact craters on planetary surfaces is essential for unraveling geological processes and the evolution of celestial bodies. Several machine learning and AI-based approaches have been proposed to detect craters on planetary surface images automatically. However, designing a robust tool for an entire complex planet such as Mars, is still an open problem. This article presents a novel approach using the Faster Region-based Convolutional Neural Network (Faster R-CNN) for such a detection. The proposed method involves the pre-processing, training and crater detection steps, which are especially designed for robustness regarding latitude and complex geomorphological features. The objectives of this studies are to (i) be robust at all latitudes and (ii) for >= 1 km diameter crater sizes. (iii) To propose an open-source and re-usable algorithm that (iv) only needs an image to run. Extensive experiments on high-resolution planetary imagery demonstrate excellent performances with an average precision AP(50)>0.82 with an intersection over union criterion IoU >= 0.5, irrespective of crater scale. For mid and high latitudes (higher than 48 degrees north and south), performance decreases down to AP(50)similar to 0.7, which is still better than the current state of the art. Loss of performance is mostly due to strong shadowing effects. Our results also highlight the versatility and potential of our robust model for automating the analysis of craters across different celestial bodies. The automated crater detection tool presented in this article is publicly available as open-source and holds great promise for future scientific research of space exploration missions.

关键词： detection algorithm Impact craters Computer vision Artificial intelligence

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Entropic detection of chromatic community structures

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JOURNAL OF COMPLEX NETWORKS 2025年第1期13卷

作者： Delaplace, Franck Univ Evry Paris Saclay Univ IBISC Lab Evry Courcouronnes France

The identification of community structure represents a central challenge in the field of complex networks. The objective is to ascertain the internal organization of agents within a network and to provide a representative network partition. Each community is presumed to comprise nodes sharing a common objective or property. The identification of these communities is typically based on the difference in connectivity density between the interior and border of a community. Indeed, nodes sharing a common purpose or property are expected to interact closely. Although this rule appears to be relevant, it nevertheless fails to address fundamental scientific problems such as disease module detection, thereby highlighting the inability to meaningfully determine communities based solely on connectivity for this situation. Consequently, another paradigm is necessary to formalize this problem accurately and detect these communities objectively. In this article, we propose a new framework to study this novel community formation property. Considering that colors represent shared properties, the problem is to maximize groups of nodes of the same color within communities. We propose a novel algorithm for detecting community structure based on a new measurement, chromatic entropy, which quantitatively assesses the community structure based on color constraint.

关键词： community structure detection algorithm colored graph

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Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm detection-the Tromsø Study

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NEUROINFORMATICS 2025年第1期23卷 1-9页

作者： Bernecker, Luca Mathiesen, Ellisiv B. Ingebrigtsen, Tor Isaksen, Jorgen Johnsen, Liv-Hege Vangberg, Torgil Riise UiT Arctic Univ Norway Dept Clin Med Tromso Norway Univ Hosp North Norway PET Imaging Ctr Tromso Norway Univ Hosp North Norway Dept Radiol Tromso Norway Univ Hosp North Norway Dept Neurol Tromso Norway Univ Hosp North Norway Dept Neurosurg Ophthalmol & Otorhinolaryngol Tromso Norway

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal. Early detection is crucial for effective intervention. In this study, we introduced a method that combines classical computer vision techniques with deep learning to detect intracranial aneurysms and ICAS in time-of-flight magnetic resonance angiography images. The process began with skull-stripping, followed by an affine transformation to align the images to a common atlas space. We then focused on the region of interest, including the circle of Willis, by cropping the relevant area. A segmentation algorithm was used to isolate the arteries, after which a patch-wise residual neural network was applied across the image. A voting mechanism was then employed to identify the presence of atrophies. Our method achieved accuracies of 76.5% for aneurysms and 82.4% for ICAS. Notably, when occlusions were not considered, the accuracy for ICAS detection improved to 85.7%. While the algorithm performed well for localized pathological findings, it was less effective at detecting occlusions, which involved long-range dependencies in the MRIs. This limitation was due to the architectural design of the patch-wise deep learning approach. Regardless, this can, in the future, be mitigated in a multi-scale patch-wise algorithm.

关键词： Intracranial Stenosis Aneurysms Deep learning detection algorithm

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Active RIS Assisted Index Modulation for Wireless Communication System

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 420-424页

作者： Wu, Yun Zhang, Xiao Bai, Enjian Hai, Han Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China

Index modulation and reconfigurable intelligent surfaces (RIS) have garnered significant attention in recent years due to their promising potential in advancing wireless communications. In this letter, we combine these two techniques and propose an amplification factor-indexed modulation (AFIM) scheme to improve the spectral efficiency in active RIS (A-RIS) aided systems, where the optimized amplification factor of A-RIS is modulated to transmit additional information bits. A closed-form expression for the average bit error probability of AFIM is derived. Furthermore, we extend AFIM to an A-RIS assisted hybrid index modulation (A-RIS-HIM) system, which incorporates reflecting modulation and receive spatial modulation. Besides, two low-complexity detectors based on greedy detection (GD) are proposed for the A-RIS-HIM system. Simulation results are provided to substantiate the effectiveness of the proposed scheme in terms of bit error rate.

关键词： Modulation Reconfigurable intelligent surfaces Indexes Signal to noise ratio Reflection Error probability Vectors Receiving antennas Detectors Bit error rate Active RIS index modulation performance analysis detection algorithm

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A fast algorithm for community detection in temporal network

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 429卷 87-94页

作者： He, Jialin Chen, Duanbing Univ Elect Sci & Technol China Web Sci Ctr Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu 611731 Peoples R China

Many complex systems can be investigated using the framework of temporal networks, which consist of nodes and edges that vary in time. The community structure in temporal network contributes to the understanding of evolving process of entities in complex system. The traditional method on dynamic community detection for each time step is independent of that for other time steps. It has low efficiency for ignoring historic community information. In this paper, we present a fast algorithm for dynamic community detection in temporal network, which takes advantage of community information at previous time step and improves efficiency while maintaining the quality. Experimental studies on real and synthetic temporal networks show that the CPU running time of our method improves as much as 69% over traditional one. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Complex systems Temporal networks Dynamic community Community structure detection algorithm

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An algorithm for the automatic detection of inclusions in an inspected object with a scanning digital X-ray imaging system (one-dimensional variant)

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RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING 2014年第6期50卷 359-368页

作者： Vorobeichikov, S. E. Udod, V. A. Klimenov, V. A. Shchetinkin, S. A. Tomsk State Univ Tomsk 634050 Russia Tomsk Polytech Univ Inst Introscopy Tomsk 634028 Russia Tech Univ State Inst Radio Engn Elect & Automat Inst Tech Facil Nondestruct Testing Moscow 119454 Russia

The mathematical model of an output digital signal from a single measuring channel of a scanning digital X-ray imaging system containing a line of detectors is presented for the case where the main type of signal distortions is the noise due to the quantum nature of radiation. This model is used to develop a one-dimensional algorithm for the automatic detection of local "critical" inclusions in an inspected object and to obtain the statistical estimates of its efficiency based on a model example.

关键词： detection algorithm inspected object local inclusions digital X-ray imaging scanning system

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Numerical Simulation for Wind Shear detection With a Glide Path Scanning algorithm Based on Wind LiDAR

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IEEE SENSORS JOURNAL 2021年第18期21卷 20248-20257页

作者： Zhao, Wen Kai Zhao, Shi Jun Shan, Yu Long Sun, Xue Jin Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410003 Peoples R China

To study the performance of the glide path scan wind shear alerting algorithm (GLYGA) for wind shear detection with wind LiDAR sensors, this paper constructed two types of simulated wind fields for wind shear detection experiments based on a MATLAB toolbox: uniform wind field and shear wind field. The uniform wind field is used to study the influence of the relative position between the wind LiDAR system and the glide path on the wind detection results;the shear wind field is used to study the effectiveness of the GLYGA. The shear wind field consists of four types: frontal, downburst, low-level jet and turbulence. The results show that both the distance from the wind LiDAR system to the runway and the angle between the laser beam and the glide path should be minimized to ensure better wind detection results while avoiding the scenario in which the laser beam runs perpendicular to the glide path;otherwise, the wind speed measured by LiDAR is 0, which will introduce great error. At the same time, the GLYGA can adequately identify the position and intensity of wind shear along the glide path of aircraft.

关键词： Laser radar Wind speed Aircraft Sensors Laser beams Airports Measurement by laser beam detection algorithm low-level wind shear numerical simulation wind lidar

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The CS algorithm: A novel method for high frequency oscillation detection in EEG

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JOURNAL OF NEUROSCIENCE METHODS 2018年 293卷 6-16页

作者： Cimbalnik, Jan Hewitt, Angela Worrell, Greg Stead, Matt Mayo Clin Mayo Syst Electrophys Lab Rochester MN USA St Annes Univ Hosp Brno Int Clin Res Ctr Brno Czech Republic

Background: High frequency oscillations (HFOs) are emerging as potentially clinically important biomarkers for localizing seizure generating regions in epileptic brain. These events, however, are too frequent, and occur on too small a time scale to be identified quickly or reliably by human reviewers. Many of the deficiencies of the HFO detection algorithms published to date are addressed by the CS algorithm presented here. New Method: The algorithm employs novel methods for: 1) normalization;2) storage of parameters to model human expertise;3) differentiating highly localized oscillations from filtering phenomena;and 4) defining temporal extents of detected events. Results: Receiver-operator characteristic curves demonstrate very low false positive rates with concomitantly high true positive rates over a large range of detector thresholds. The temporal resolution is shown to be +/-similar to 5 ms for event boundaries. Computational efficiency is sufficient for use in a clinical setting. Comparison with existing methods: The algorithm performance is directly compared to two established algorithms by Staba (2002) and Gardner (2007). Comparison with all published algorithms is beyond the scope of this work, but the features of all are discussed. All code and example data sets are freely available. Conclusions: The algorithm is shown to have high sensitivity and specificity for HFOs, be robust to common forms of artifact in EEG, and have performance adequate for use in a clinical setting. (C) 2017 Elsevier B.V. All rights reserved.

关键词： High frequency oscillations HFO Ripples Frequency dominance detection algorithm

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