Journal articles on the topic 'Intrusive windows'
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He, Xi, Heng Dong, Wanli Yang, and Jun Hong. "A Novel Denoising Auto-Encoder-Based Approach for Non-Intrusive Residential Load Monitoring." Energies 15, no. 6 (March 21, 2022): 2290. http://dx.doi.org/10.3390/en15062290.
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Mounting concerns pertaining to energy efficiency have led to the research of load monitoring. By Non-Intrusive Load Monitoring (NILM), detailed information regarding the electric energy consumed by each appliance per day or per hour can be formed. The accuracy of the previous residential load monitoring approach relies heavily on the data acquisition frequency of the energy meters. It brings high overall cost issues, and furthermore, the differentiating algorithm becomes much more complicated. Based on this, we proposed a novel non-Intrusive residential load disaggregation method that only depends on the regular data acquisition speed of active power measurements. Additionally, this approach brings some novelties to the traditionally used denoising Auto-Encoder (dAE), i.e., the reconfiguration of the overlapping parts of the sliding windows. The median filter is used for the data processing of the overlapping window. Two datasets, i.e., the Reference Energy Disaggregation Dataset (REDD) and TraceBase, are used for test and validation. By numerical testing of the real residential data, it proves that the proposed method is superior to the traditional Factorial Hidden Markov Model (FHMM)-based approach. Furthermore, the proposed method can be used for energy data, disaggregation disregarding the brand and model of each appliance.
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Massoz, Quentin, Jacques Verly, and Marc Van Droogenbroeck. "Multi-Timescale Drowsiness Characterization Based on a Video of a Driver’s Face." Sensors 18, no. 9 (August 25, 2018): 2801. http://dx.doi.org/10.3390/s18092801.
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Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. In practice, the least intrusive, physiology-based approach is to remotely monitor, via cameras, facial expressions indicative of drowsiness such as slow and long eye closures. Since the system’s decisions are based upon facial expressions in a given time window, there exists a trade-off between accuracy (best achieved with long windows, i.e., at long timescales) and responsiveness (best achieved with short windows, i.e., at short timescales). To deal with this trade-off, we develop a multi-timescale drowsiness characterization system composed of four binary drowsiness classifiers operating at four distinct timescales (5 s, 15 s, 30 s, and 60 s) and trained jointly. We introduce a multi-timescale ground truth of drowsiness, based on the reaction times (RTs) performed during standard Psychomotor Vigilance Tasks (PVTs), that strategically enables our system to characterize drowsiness with diverse trade-offs between accuracy and responsiveness. We evaluated our system on 29 subjects via leave-one-subject-out cross-validation and obtained strong results, i.e., global accuracies of 70%, 85%, 89%, and 94% for the four classifiers operating at increasing timescales, respectively.
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Siqueira Cassiano, Marcella. "China’s Hukou Platform: Windows into the Family." Surveillance & Society 17, no. 1/2 (March 31, 2019): 232–39. http://dx.doi.org/10.24908/ss.v17i1/2.13125.
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When news media in the UK and US discuss China’s surveillance networks, it is often to imply that the Chinese government is creating a “techno-authoritarian state” to track and monitor its citizens. News outlets, however, are missing a larger point. The specific problem with China’s surveillance apparatus is not that it is technologically “totalizing” and “intrusive,” but that it relies on a newly digitized information platform that connects surveillance subjects to information about their households and family members, allowing the simultaneous identification and monitoring of everyone in each kinship network. Referred to as the Household Register or hukou, this platform is the backbone of China’s “surveillant assemblage” (Haggerty and Ericson 2000). Until the late 1990s when it was digitalized, hukou was an individually separate and distinct surveillance system that contained both general and detailed information about a household and its members. With digitalization, hukou became a platform that integrates different computer systems and databases. CCTV surveillance that involves facial recognition and Internet surveillance practices are connected to, and supported by, information from hukou. In the case of CCTV surveillance, cameras equipped with facial recognition features match the face of surveillance subjects with their ID and trace them back to their families. As for Internet surveillance, the connection between hukou and surveillance subjects happens via telephone number. Access to the Internet and social media platforms such as WeChat, SinaWeibo, and e-mail services requires a telephone number purchased with a government-issued ID card, which is connected to a household register and, therefore, the telephone card owner’s family. Chinese law enforcement’s ability to treat individual Internet users also as “collective units” represents the most distinctive feature of Chinese surveillance, an unlimited source of coercion for the Communist Party to reproduce itself as the ruling party.
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Liu, Lin, Yuehua Jiang, Xun Zhou, Yun Li, Quanping Zhou, Jingwen Su, Junyuan Jia, Guoqiang Yang, and Yang Jin. "Long-Lasting Boiling-Wells: Geochemical Windows into the Tectonic Activity of the Maodong Fault (China)." Water 14, no. 3 (January 29, 2022): 427. http://dx.doi.org/10.3390/w14030427.
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The Maodong Fault (China) was mainly active during the Late Pleistocene. However, in the past century, numerous destructive earthquakes have occurred along the fault zone, indicating its continuing activity. Therefore, refined monitoring of the tectonic activity along the fault is required. Boiling-Wells located in the Maodong Fault Zone were selected for this purpose. The parameters, including the rare earth elements (REE) and gas components, such as CO2, Rn, and Total Volatile Organic Compounds (TVOC), in the wells were analyzed. By combining field observations with the analytical data, we constrained the relationships between the anomalies of the hydrochemical composition and the gas composition in the Boiling-Wells and the Maodong Fault: (1) CO2 and TVOC in the Boiling-Wells originated from Cenozoic magmatism and associated intrusive rocks. High concentrations of Rn are closely linked to tectonic activities of the Maodong Fault. CO2, TVOC, and Rn are all transported to the Boiling-Wells along the Maodong Fault, with CO2 acting as a carrier gas for Rn. (2) REE in the Boiling-Wells was mainly sourced from CO2 fluids that originated from deep-seated Cenozoic magmas and intrusive rocks. The concentrations of the REE and their distribution patterns were controlled by the input of CO2 fluids and by epigenetic processes. (3) The abnormally high contents of Ca2+, HCO3−, Pb2+, and Al3+ in the Boiling-Wells are attributed to the migration of externally-derived (deep) CO2 fluids through the Maodong Fault. (4) The anomalies of the gaseous (Rn, CO2, and TVOC) and hydrochemical components (Ca2+, HCO3−, Pb2+, Al3+, ∑REE, and REE patterns) in the Boiling-Wells are closely related to the tectonic activity of the Maodong Fault. Therefore, the long-lasting Boiling-Wells provide an excellent geochemical window into the evolution of the Maodong Fault. Our study documents that the contents and variations of specific hydrochemical and gaseous components of Boiling-Wells are well-suited geochemical tracers to identify and characterize the tectonic activity of the Maodong Fault. This method is also applicable for the monitoring of tectonic activities of major faults zones with comparable preconditions worldwide.
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Ashton, K. E., J. F. Lewry, L. M. Heaman, R. P. Hartlaub, M. R. Stauffer, and H. T. Tran. "The Pelican Thrust Zone: basal detachment between the Archean Sask Craton and Paleoproterozoic Flin Flon Glennie Complex, western Trans-Hudson Orogen." Canadian Journal of Earth Sciences 42, no. 4 (April 1, 2005): 685–706. http://dx.doi.org/10.1139/e04-035.
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The Pelican Thrust Zone is a 37 km-wide recrystallized mylonite zone, along which Paleoproterozoic arc volcano-plutonic and derived sedimentary rocks of the Flin Flon Glennie Complex were thrust over an Archean package (Jan Lake Complex) consisting of ca. 3.1 Ga calc-alkaline orthogneisses, pelitic migmatites, and a ca. 2.45 Ga tholeiitic charnockitenorite intrusive suite. A regional northeast-plunging stretching lineation and a variety of kinematic indicators imply southwesterly transport, matching that of other coeval shallower crustal-level structures observed throughout the northern Flin Flon Domain and southern flank of the Kisseynew Domain (e.g., SturgeonWeir and Annabel Lake shear zones). Subsequent east-side-up displacement on the Tabbernor Fault, together with domal fold interference, has exposed the Pelican Window and mantling mylonite zone, where it has been seismically profiled along Lithoprobe transects. Tectonic windows also expose Archean rocks in the Glennie Domain to the west, where similar southwest-verging, recrystallized mylonite zones have been documented. Together, these zones record collision and underplating of the Flin Flon Glennie Complex by the Archean Sask Craton during prolonged protocontinentcontinent collision over a minimum 18261805 Ma interval.
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Zhang, Guoming, Xiaoyu Ji, Yanjie Li, and Wenyuan Xu. "Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid." Sensors 20, no. 13 (June 28, 2020): 3635. http://dx.doi.org/10.3390/s20133635.
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As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.
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Shi, Yongtao, Xiaodong Zhao, Fan Zhang, and Yaguang Kong. "Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot." Energies 15, no. 20 (October 21, 2022): 7800. http://dx.doi.org/10.3390/en15207800.
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Non-Intrusive Load Monitoring (NILM) is an effective energy consumption analysis technology, which just requires voltage and current signals on the user bus. This non-invasive monitoring approach can clarify the working state of multiple loads in the building with fewer sensing devices, thus reducing the cost of energy consumption monitoring. In this paper, an NILM method combining adaptive Recurrence Plot (RP) feature extraction and deep-learning-based image recognition is proposed. Firstly, the time-series signal of current is transformed into a threshold-free RP in phase space to obtain the image features. The Euclidean norm in threshold-free RP is scaled exponentially according to the voltage and current correlation to reflect the working characteristics of different loads adaptively. Afterwards, the obtained adaptive RP features can be mapped into images using the corresponding pixel value. In the load identification stage, an advanced computer vision deep network, Hierarchical Vision Transformer using Shifted Windows (Swin-Transformer), is applied to identify the adaptive RP images. The proposed solution is extensively verified by four real, measured load signal datasets, including industrial and household power situations, covering single-phase and three-phase electrical signals. The numerical results demonstrate that the proposed NILM method based on the adaptive RP can effectively improve the accuracy of load detection.
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Ma, Yongfeng, Zhuopeng Xie, Shuyan Chen, Ying Wu, and Fengxiang Qiao. "Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion." International Journal of Environmental Research and Public Health 19, no. 1 (December 29, 2021): 348. http://dx.doi.org/10.3390/ijerph19010348.
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Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.
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Saralegui, Unai, Miguel Antón, Olatz Arbelaitz, and Javier Muguerza. "Smart Meeting Room Usage Information and Prediction by Modelling Occupancy Profiles." Sensors 19, no. 2 (January 16, 2019): 353. http://dx.doi.org/10.3390/s19020353.
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The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.
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Corriveau, Louise, and Otto van Breemen. "Docking of the Central Metasedimentary Belt to Laurentia in geon 12: evidence from the 1.17-1.16 Ga Chevreuil intrusive suite and host gneisses, Quebec." Canadian Journal of Earth Sciences 37, no. 2-3 (April 2, 2000): 253–69. http://dx.doi.org/10.1139/e00-004.
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The Chevreuil intrusive suite (1.17-1.16 Ga) represents a chronological field marker of regional extent that intruded the Central Metasedimentary Belt in the western Grenville Province of Quebec after peak metamorphism. Style and site of magma emplacement, and extent of deformation of Chevreuil plutons and dykes permit unravelling of the early Grenvillian evolution of the belt with respect to cratonal North America. The suite comprises a series of vertically layered gabbro stocks and monzonite-diorite-gabbro sheet intrusions, and a swarm of microdiorite dykes that cut across gneisses. The dykes display systematic variations in extent of deformation across the belt. We targeted U-Pb geochronology on gneisses within the identified strain windows; they preserve the record of a ca. 1.20 Ga high pressure-temperature (P-T) metamorphic event. The sheet intrusions define magmatic corridors all along, and concordant with, the western, northern, and eastern tectonic boundaries of the belt. The concordant and elongate shape of these bodies results from emplacement, not deformation. Chevreuil magmas thus sealed the belt boundaries largely in their current positions, with the implication that docking of Elzevirian and pre-Elzevirian terranes with cratonal North America predates 1.17 Ga. We interpret the 1.20 Ga metamorphism as evidence for the initiation of Grenvillian continent-continent collision during the culmination of the Elzevirian orogeny at ca. 1.22 Ga. Emplacement-related fabrics indicate that the Chevreuil suite and the coeval Morin anorthosite suite intruded during renewed orogenesis. This orogenic pulse (Shawinigan) is not accretionary, but represents a strongly partitioned, compressive, intraplate reactivation event.
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Coluzzi, Davide, Giuseppe Baselli, Anna Maria Bianchi, Guillermina Guerrero-Mora, Juha M. Kortelainen, Mirja L. Tenhunen, and Martin O. Mendez. "Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device." Sensors 22, no. 14 (July 15, 2022): 5295. http://dx.doi.org/10.3390/s22145295.
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Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.
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Doughty, P. T., R. A. Price, and R. R. Parrish. "Geology and U-Pb geochronology of Archean basement and Proterozoic cover in the Priest River complex, northwestern United States, and their implications for Cordilleran structure and Precambrian continent reconstructions." Canadian Journal of Earth Sciences 35, no. 1 (January 1, 1998): 39–54. http://dx.doi.org/10.1139/e97-083.
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Precambrian basement rocks exposed within tectonic windows in the North American Cordillera help to define the Precambrian crustal structure of western North America and possible reconstructions of the Late Proterozoic supercontinent Rodinia. New geologic mapping and U-Pb dating in the infrastructure of the Priest River metamorphic complex, northern Idaho, documents the first Archean basement (2651 ± 20 Ma) north of the Snake River Plain in the North American Cordillera. The Archean rocks are exposed in the core of an antiform and mantled by a metaquartzite that may represent the nonconformity between basement and the overlying Hauser Lake gneiss, which is correlated with the Prichard Formation of the Belt Supergroup. A structurally higher sheet of augen gneiss interleaved with the Hauser Lake gneiss yields a U-Pb zircon crystallization age somewhat greater than 1577 Ma. The slivers of augen gneiss were tectonically interleaved with the surrounding Hauser Lake gneiss near the base of the Spokane dome mylonite zone, which arches across this part of the Priest River complex. We conclude that the Spokane dome mylonite zone lies above the Archean basement-cover contact and that it was, in part, equivalent to the basal décollement of the Rocky Mountain fold and thrust belt. New U-Pb dates on metamorphic monazite and xenotime reveal peak metamorphism at ca. 72 Ma, compatible with movement along the Spokane dome mylonite zone at that time. The Archean basement could be interpreted as the western extension of the Hearne province, or a new Archean basement terrane separated from the Hearne province by an Early Proterozoic suture. The unique assemblage of 2.65 Ga basement, ~1.58 Ga felsic intrusive rocks, and the Middle Proterozoic Belt Supergroup can be used as a piercing point for the identification of the conjugate margin to Laurentia. Our new dating supports previous correlations of Australia's Gawler craton (2.55-2.65 Ga) and its 1590 Ma plutons with the Priest River complex basement gneisses. The Priest River complex basement may be a piece of eastern Australia stranded during rifting of the supercontinent Rodina in the Late Proterozoic.
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Hamed, Eman Esmaeel, and Muna Majeed Lafta. "INTRUSION WINDOWS XP BY BACKDOOR TOOL." Journal of Al-Nahrain University Science 11, no. 3 (December 1, 2008): 175–78. http://dx.doi.org/10.22401/jnus.11.3.21.
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Li, Yanyan, Haiguo Yin, Xin Deng, Linfeng Liang, and Yongchao Zhai. "Simulation Research on Ventilation Control of Gaseous Pollutants in Urban Sentry Boxes." E3S Web of Conferences 356 (2022): 04040. http://dx.doi.org/10.1051/e3sconf/202235604040.
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The tiny spaces of sentry boxes in cities, such as mobile security guards, highway toll booths, etc., are mostly located beside roads with harsh outdoor environments. Due to office demands, work windows often need to be kept or frequently opened. The intrusion of outdoor pollutants through the windows leads to the deterioration of the indoor air quality, and threatens the health of employees. This paper takes the gaseous pollutant NO2 as the representative and discusses the effective ventilation design scheme for improving the air quality in the sentry box with openings using two attached ventilation modes as the carrier. Taking the vertical wall-attached ventilation as an example, the formation of the air curtain at the window hole and its barrier performance to outdoor pollutants were studied. The conclusion is that when the air supply velocity is sufficient to form a complete air curtain at the window hole, it can effectively block the pollutants. The horizontal wall-attached ventilation shows that clean air is delivered to the space with openings, and the indoor air quality can also be well improved due to the dilution effect, but the effect of positive pressure control is not obvious due to the large opening. The conclusions can provide guidance for the ventilation design of sentry boxes with openings.
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Xie, Haoyang. "Forensic Analysis of Windows Registry Against Intrusion." International Journal of Network Security & Its Applications 4, no. 2 (March 31, 2012): 121–34. http://dx.doi.org/10.5121/ijnsa.2012.4209.
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Armstrong, Richard Lee, Randall R. Parrish, Peter van der Heyden, Krista Scott, Dita Runkle, and Richard L. Brown. "Early Proterozoic basement exposures in the southern Canadian Cordillera: core gneiss of Frenchman Cap, Unit I of the Grand Forks Gneiss, and the Vaseaux Formation." Canadian Journal of Earth Sciences 28, no. 8 (August 1, 1991): 1169–201. http://dx.doi.org/10.1139/e91-107.
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The protolith age of high-grade metamorphic rocks exposed in structurally deep parts of the Omineca Crystalline Belt has been the subject of investigation and controversy for decades. We have applied multiple isotopic dating techniques to rocks of three structural culminations: the Monashee complex (which includes the Frenchman Cap and Thor–Odin gneiss domes), the Grand Forks horst, and the Vaseaux Formation, which lies in the footwall of the Okanagan Valley fault.Frenchman Cap core gneisses contain highly radiogenic Sr that scatters about a 2206 ± 117 Ma (1σ) Rb–Sr isochron with 87Sr/86Sr initial ratio of 0.700 ± 0.002. Monazite and zircon dates for the same rocks are 1851 ± 7 to 2103 ± 16 Ma (only U–Pb dates are given with 2σ errors), with lower intercepts from about 100 to 300 Ma. Sm–Nd whole-rock and crustal-residence (TDM) dates are 2.3 ± 0.2 Ga. Mafic–felsic layering in the core gneiss is also of Early Proterozoic age. There is no geochronometric evidence for Late Proterozoic or Mesozoic migmatization.Frenchman Cap mantling gneisses, including samples from above the Monashee décollement, have radiogenic Sr and unradiogenic Nd compositions that are not consistent with current inferences of a Late Proterozoic to Paleozoic depositional age. Two intrusive granitic rocks, which cut mantling gneiss, are either Early Proterozoic or Mesozoic–Cenozoic with a Proterozoic Sr isotopic signature acquired by assimilation of core gneiss. One other intrusive studied is probably Paleocene Ladybird granite. The age of the mantling gneiss is not yet consistently resolved.Grand Forks Gneiss Unit I paragneiss gives radiogenic whole-rock Sr, zircon U–Pb upper intercept, and Sm–Nd whole-rock crustal-residence dates of 1.7 ± 0.4 Ga, 1681 ± 3 Ma (2σ, but the apparent high precision is very dependent on the assumption made about the time of Pb loss), and 1.9 ± 0.3 Ga, respectively. Unit II and younger Grand Forks Gneiss units are Late Proterozoic or Phanerozoic. All isotope systems have been considerably reset on a centimetre to metre scale by Mesozoic–Cenozoic regional metamorphism. Grand Forks Sr, Pb, and Nd isotope data are much like those for Spokane area pre-Purcell basement.Vaseaux Formation micaceous schist and gneiss give radiogenic whole-rock Sr, zircon U–Pb upper intercept, and Sm–Nd crustal-residence dates of 2.1 ± 0.6 Ga, 1899 ± 49 Ma (2σ), and 2.2 ± 0.1 Ga, respectively. Hornblende-bearing schist and gneiss contain much less radiogenic Sr and more radiogenic Nd. The latter are either tectonic intercalations of Late Proterozoic to Paleozoic eugeosynclinal rocks or Mesozoic–Cenozoic mixtures of mantle-derived magma and older crustal rock. The Vaseaux Formation paragneiss is similar isotopically to paragneiss in the Frenchman Cap core gneiss. This may indicate a similar age, or that Vaseaux sedimentary rocks could be much younger and isochemically derived from a basement of Frenchman Cap character. The first alternative is favored because the three isotope systems are usually not preserved in unison through sedimentary processes. Sr isotopes, in particular, do not usually preserve a provenance age.In all three areas, late Mesozoic to early Cenozoic metamorphic monazite, hornblende, muscovite, and biotite dates provide a record of cooling from a Cretaceous to Paleocene culmination of regional metamorphism, with particularly rapid cooling during Paleocene to Eocene crustal extension and tectonic unroofing.The localities studied are tectonic windows on structural culminations that expose basement that we infer to be part of North America. Their ages fit the pattern of basement ages established for the stable craton. Their extent is consistent with the reconstruction of compressed miogeoclinal rocks. The eastern half of the Cordilleran region on both sides of the United States – Canada border is underlain by Early Proterozoic basement that was attenuated in Late Proterozoic time, compressed during Mesozoic–Cenozoic orogeny, and finally extended in early Cenozoic collapse of the thickened crust. During Mesozoic–Cenozoic orogeny the sedimentary cover of that basement was pushed approximately 200 km eastward and replaced by allochthonous terranes. The tectonic displacements documented in the southern Canadian Cordillera are truly exceptional.
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Zheng, Hengjie, Fei Li, Hao Cai, and Kai Zhang. "Non-intrusive measurement method for the window opening behavior." Energy and Buildings 197 (August 2019): 171–76. http://dx.doi.org/10.1016/j.enbuild.2019.05.052.
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Jantunen, Juha, and Kimmo Saarinen. "Intrusion of airborne pollen through open windows and doors." Aerobiologia 25, no. 3 (June 13, 2009): 193–201. http://dx.doi.org/10.1007/s10453-009-9124-8.
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Chabab, Elena, Michael Kühn, and Thomas Kempka. "Upwelling mechanisms of deep saline waters via Quaternary erosion windows considering varying hydrogeological boundary conditions." Advances in Geosciences 58 (November 14, 2022): 47–54. http://dx.doi.org/10.5194/adgeo-58-47-2022.
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Abstract. Intrusion of deep saline waters into freshwater aquifers does not only endanger the regional drinking water supply, but also rivers and stagnant waters and their fauna are threatened by salinisation. The upwelling of highly mineralised saline waters in large parts of the North German Basin is favoured by the presence of Elsterian glacial erosion windows in the Lower Oligocene Rupelian Clay, the most important hydraulic confining unit in this region. Lower precipitation rates and decreasing groundwater levels as a consequence of global climate change, but also anthropogenic interventions, such as increasing extraction rates or the use of the deep geologic subsurface as a reservoir, decrease the pressure potential in the freshwater column and may possibly accelerate this primarily geogenic salinisation process in the coming years. Density-driven flow and transport modelling was performed in the scope of the present study to investigate the upwelling mechanisms of deep saline waters across Quaternary window sediments in the Rupelian. Simulation results show that the interactions between the groundwater recharge rate and anthropogenic interventions such as extraction rates of drinking water wells or the utilisation of the deep subsurface, have a significant influence on the groundwater pressure potential in the freshwater aquifer and associated saltwater upwelling. In all scenarios, salinisation is most severe in the sediments of the erosion windows. Hydraulically conductive faults also intensify salinisation if located nearside erosion windows or induce a more distributed or localised salinisation in aquifers with drinking water relevance in areas that do not intersect with erosion windows. A decline in groundwater recharge thereby significantly favours upward saltwater migration. The simulation scenarios further show that a decrease in groundwater recharge also results in freshwater salinisation occurring up to 10 years earlier, which underlines the need for waterworks to initiate effective countermeasures quickly and in time.
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Kamboj, Sunjeev K., An Tong Gong, ZhiHui Sim, Adrihani A. Rashid, Ami Baba, Georges Iskandar, Ravi K. Das, and H. Valerie Curran. "Reduction in the occurrence of distressing involuntary memories following propranolol or hydrocortisone in healthy women." Psychological Medicine 50, no. 7 (May 14, 2019): 1148–55. http://dx.doi.org/10.1017/s0033291719001028.
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AbstractBackgroundPharmacological treatments targeting the neuroendocrine stress response may hold special promise in secondary prevention of posttraumatic stress disorder (PTSD). However, findings from clinical trials have been inconsistent and the efficacy of specific drugs, their temporal window of efficacy, effective doses and the characteristics of likely treatment responders remain unclear.MethodUsing an experimental human model of distressing involuntary memory formation, we compare the effects of two drugs that have theoretical or empirical support as secondary preventive agents in PTSD. Eighty-eight healthy women (average age: 23.5 years) received oral propranolol (80 mg), hydrocortisone (30 mg), or matched placebo immediately after viewing a ‘trauma film’. They then completed daily, time-stamped intrusion diaries for 1 week, at the end of which, voluntary memory was tested.ResultsWhile neither drug affected voluntary memory for the trauma narrative, propranolol treatment was associated with 42% fewer, and hydrocortisone with 55% fewer intrusions across the week, relative to placebo. Additionally, propranolol reduced general trauma-like symptoms, and post-drug cortisol levels were negatively correlated with intrusion frequency in the hydrocortisone group.ConclusionsOverall, this study shows substantial reductions in intrusive memories and preserved voluntary narrative-declarative memory following either propranolol or hydrocortisone in an experimental model of psychological trauma. As such, despite some inconsistencies in clinical trials, our findings support continued investigation of propranolol and hydrocortisone as secondary preventive agents for re-experiencing symptoms of PTSD. The findings also suggest that it is critical for future research to identify the conditions governing the preventive efficacy of these drugs in PTSD.
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Zevenhoven, Ron, and Martin Fält. "Radiative cooling through the atmospheric window: A third, less intrusive geoengineering approach." Energy 152 (June 2018): 27–33. http://dx.doi.org/10.1016/j.energy.2018.03.084.
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Yang, Wonyoung, and Hyeun Jun Moon. "Effects of indoor water sounds on intrusive noise perception and speech recognition in rooms." Building Services Engineering Research and Technology 39, no. 6 (April 17, 2018): 637–51. http://dx.doi.org/10.1177/0143624418769187.
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While water sounds have been used for soundscape improvement, little is known about their applicability in indoor environments. In order to investigate the effects of indoor water sounds on noise perception, a simple indoor water fountain system was used to produce water sounds over three different types of indoor intrusive noise (traffic noise, higher frequency dominated noise of a chair scraping the floor above, lower frequency dominated impact noise of a man running on the floor above) and speech in a test laboratory. Intrusive noise perception (annoyance and pleasantness) and speech recognition (KS-MWL-A) were assessed with three water sound levels (40, 50, 60 dBA) at two exposure times (immediate and 50 min) of water sounds by 54 participants. Short-term exposure to indoor water sounds improved the pleasantness of intrusive noise without increasing annoyance except lower frequency dominated impact noise. The increase in exposure time to indoor water sounds did not affect intrusive noise perception and speech recognition. The water to noise ratio significantly affected annoyance and pleasantness of traffic noise only; however, the level of water sounds did not significantly affect intrusive noise perception. Indoor water sounds can be used to improve intrusive noise perception except lower frequency dominated floor impact noise with no adverse effects on speech recognition dependent upon the speech to water sound ratio. Practical application: This simple indoor water fountain can be directly applied to small offices or rooms to improve intrusive noise perception. When the simple fountain produces water sounds in a room, pleasantness of traffic noise throughout window openings or higher frequency dominated noise such as chair scraping noise can be improved without increment of annoyance and decrement of speech recognition. Short-term exposure to indoor water sounds is effective to increase pleasantness of the intrusive noises.
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Liang, Xiang San, and Allan R. Robinson. "A Study of the Iceland–Faeroe Frontal Variability Using the Multiscale Energy and Vorticity Analysis." Journal of Physical Oceanography 34, no. 12 (December 1, 2004): 2571–91. http://dx.doi.org/10.1175/jpo2661.1.
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Abstract The multiscale nonlinear interactive Iceland–Faeroe frontal (IFF) variability during 14–22 August 1993 is investigated for complex dynamics with the localized multiscale energy and vorticity analysis (MS-EVA). In terms of multiscale window transform, the cold meandering intrusion observed in the IFF experiment is represented on a mesoscale window. The resulting mesoscale energetics for the deep layer show an isolated center of transfer of potential energy from the large-scale window into the mesoscale window in the study domain. This large-to-mesoscale potential energy transfer, or BC for short, is a baroclinic instability indicator by the MS-EVA-based stability theory. Signatures on other energetics maps and the reconstructed mesoscale structures all support this baroclinic instability. On the BC map, the transfer hotspot originally resides near the western boundary. It travels along the front into the interior domain in a form of convective instability and then, on 19 August, changes into another instability that is absolute in character. Correspondingly, disturbances switch from a spatial growing pattern into a time growing mode, culminating on 21 August, the day just before the intrusion matures. The whole process lasts for about five days, limited within a small horizontal region and beneath a depth of approximately 150 m. By interaction analysis, the energy locally gained from this process goes to the submesoscale window as well, but most of it remains in the mesoscale window, serving to fuel the growth of the meandering intrusion.
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Zimba, Aaron, and Mumbi Chishimba. "Exploitation of DNS Tunneling for Optimization of Data Exfiltration in Malware-free APT Intrusions." Zambia ICT Journal 1, no. 1 (December 11, 2017): 51–56. http://dx.doi.org/10.33260/zictjournal.v1i1.26.
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One of the main goals of targeted attacks include data exfiltration. Attackers penetrate systems using various forms of attack vectors but the hurdle comes in exfiltrating the data. APT attackers even reside in a host for long periods of time whilst seeking the best option to exfiltrate data. Most data exfiltration techniques are prone to detection by intrusion detection system. Therefore, data exfiltration methodologies that generate little noise if any at all are attractive to attackers and can go undetected for long periods owing the low threshold of generated noise in form network traffic and system calls. In this paper, we present malware-free intrusion, an attack methodology which does not explicitly use malware to exfiltrate data. Our attack structure exploits the use of system services and resources not limited to RDP, PowerShell, Windows accessibility backdoor and DNS tunneling. Results show that it’s possible to exfiltrate data from vulnerable hosts using malwarefree intrusion as an infection vector and DNS tunneling as a data exfiltration technique. We test the attack on both Windows and Linux system over different networks. Mitigation techniques are suggested based on traffic analysis captured from the established secure DNS tunnels on the network.
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Tobin, Donald L., and Michael S. Ware. "Using a windows attack intRusion emulator (AWARE) to teach computer security awareness." ACM SIGCSE Bulletin 37, no. 3 (September 2005): 213–17. http://dx.doi.org/10.1145/1151954.1067504.
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Ronal Hadi, Yarmis Yuliana, and Hanriyawan Adnan Mooduto. "Deteksi Ancaman Keamanan Pada Server dan Jaringan Menggunakan OSSEC." JITSI : Jurnal Ilmiah Teknologi Sistem Informasi 3, no. 1 (March 31, 2022): 8–15. http://dx.doi.org/10.30630/jitsi.3.1.58.
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Kejahatan cyber dapat menyerang jaringan komputer, menyusup kedalam jaringan mengambil data-data rahasia dan melumpuhkan sistem jaringan komputer. Dalam mengatasi kejahatan yang akan terjadi, dibutuhkan sistem yang dilengkapi firewall dan Intrusion Detection System (IDS). Firewall dan IDS sebagai fitur keamanan jaringan yang dapat melindungi server, jaringan, dan memblok serangan. Fitur firewall dan IDS dapat diterapkan dalam Tools OSSEC. OSSEC merupakan IDS berbasis open-source (host-based intrusion detection system/HIDS) yang mampu melakukan analisis log, pengecekan integritas, pemantauan registri Windows, deteksi rootkit, peringatan berbasis waktu, dan respons aktif. OSSEC mampu memantau satu server atau ribuan server dalam mode server/agent
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Li, Xianfeng, Sen Xu, and Xiaopeng Hua. "Pattern Recognition of Grating Perimeter Intrusion Behavior in Deep Learning Method." Symmetry 13, no. 1 (January 6, 2021): 87. http://dx.doi.org/10.3390/sym13010087.
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An intrusion behavior recognition method based on deep learning is proposed in this paper in order to improve the recognition accuracy of raster perimeter intrusion behavior. The Mach–Zehnder fiber optic interferometer was used to collect the external vibration signal sensing unit, capture the external vibration signal, use the cross-correlation characteristic method to obtain the minimum frame length of the fiber vibration signal, and preprocess the intrusion signal according to the signal strength. The intrusion signals were superimposed and several sections of signals were intercepted by fixed window length; the spectrum information is obtained by Fourier transform of the intercepted stationary signals. The convolution neural network was introduced into the pattern recognition of the intrusion signals in the optical fiber perimeter defense zone, and the different characteristics of the intrusion signals were extracted, so as to realize the accurate identification of different intrusion signals. Experimental results showed that this method was highly sensitive to intrusion events, could effectively reduce the false alarm rate of intrusion signals, and could improve the accuracy and efficiency of intrusion signal recognition.
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Tsuge, Yusuke, and Hidema Tanaka. "Intrusion Detection System Using Discrete Fourier Transform with Window Function." International Journal of Network Security & Its Applications 8, no. 2 (March 30, 2016): 23–34. http://dx.doi.org/10.5121/ijnsa.2016.8202.
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Ramos, Raúl Gómez, Jaime Duque Domingo, Eduardo Zalama, and Jaime Gómez-García-Bermejo. "Daily Human Activity Recognition Using Non-Intrusive Sensors." Sensors 21, no. 16 (August 4, 2021): 5270. http://dx.doi.org/10.3390/s21165270.
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In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.
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Gu, Ming. "Anomaly Detection Based on Chi-Square Statistic Technology in Computer Information System." Applied Mechanics and Materials 462-463 (November 2013): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.1046.
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Algorithm and principle of anomaly detection technique based on a chi-square statistic was designed. In order to verify the effect of principle and algorithm, testing data of sample of Windows7 and Mac OS system was presented and compared. The results of this study show that chi-square technology achieves the 0% false alarm rate and the 100% detection rate for abnormal intrusion scenarios. All intrusion scenarios are detected at the first or second audit event.
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Hasan, Md Mehedi, Dhiman Chowdhury, and Md Ziaur Rahman Khan. "Non-Intrusive Load Monitoring Using Current Shapelets." Applied Sciences 9, no. 24 (December 8, 2019): 5363. http://dx.doi.org/10.3390/app9245363.
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Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.
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Badawi, Amalia, David Berle, Kris Rogers, and Zachary Steel. "Do Cognitive Tasks Reduce Intrusive-Memory Frequency After Exposure to Analogue Trauma? An Experimental Replication." Clinical Psychological Science 8, no. 3 (March 17, 2020): 569–83. http://dx.doi.org/10.1177/2167702620906148.
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Cognitive task interventions that interfere with visuospatial working memory during the memory consolidation window hold promise for reducing intrusive memories in trauma-exposed people. Our study provides an independent replication study to test and verify findings that have primarily originated from a single research group. We hypothesized that participants engaging in a visuospatial task (cognitive task intervention including Tetris or D-Corsi) following a trauma-film paradigm (TFP) would report fewer intrusive memories over the course of a week compared with control participants. Participants ( N = 110) were randomly assigned to an experimental condition after viewing the TFP. Generalized linear mixed models indicated that the cognitive task including Tetris was associated with fewer intrusions for the TFP compared with both the D-Corsi-intervention and control conditions. Our findings are congruent with existing literature indicating that cognitive tasks, such as an intervention including Tetris, may promote effective memory consolidation after exposure to a potentially traumatic event.
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Bowins, R. J., and L. M. Heaman. "Age and timing of igneous activity in the Temagami greenstone belt, Ontario: a preliminary report." Canadian Journal of Earth Sciences 28, no. 11 (November 1, 1991): 1873–76. http://dx.doi.org/10.1139/e91-167.
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The southernmost remnants of Archean supracrustal and intrusive rocks in eastern Ontario are exposed through a window in the Early Proterozoic Huronian Supergroup near the town of Temagami. U–Pb zircon ages from this area indicate the presence of some of the oldest felsic magmatism so far discovered in this portion of the Superior Province. The Iceland Lake pluton (2736 ± 2 Ma) and a nearby rhyolite flow ([Formula: see text]) are contemporaneous, which establishes that at least some of the intrusive rocks in the region are synvolcanic and coeval with the oldest volcanic cycle. The youngest plutonic activity is the emplacement of a late rhyolite porphyry dike at 2687 ± 2 Ma, an age that is bracketed by the 2675–2700 Ma emplacement ages of late internal plutons found throughout the Abitibi Subprovince. The 2736 Ma dates, however, are older than the nearest portion of the exposed Abitibi, some 120 km to the north near Kirkland Lake.
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Chen, Wei-Cheng, and Keh-Chin Chang. "PIV measurements in near-wake turbulent regions." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840026. http://dx.doi.org/10.1142/s0217984918400262.
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Particle image velocimetry (PIV) is a non-intrusive optical diagnostic and must be made of the seedings (foreign particles) instead of the fluid itself. However, reliable PIV measurement of turbulence requires sufficient numbers of seeding falling in each interrogation window of image. A gray-level criterion is developed in this work to check the attainment of statistically stationary status of turbulent flow properties. It is suggested that the gray level of no less than 0.66 is used as the threshold for reliable PIV measurements in the present near-wake turbulent regions.
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Subarkah, Darwinaji, Angus L. Nixon, Monica Jimenez, Alan S. Collins, Morgan L. Blades, Juraj Farkaš, Sarah E. Gilbert, Simon Holford, and Amber Jarrett. "Constraining the geothermal parameters of in situ Rb–Sr dating on Proterozoic shales and their subsequent applications." Geochronology 4, no. 2 (September 6, 2022): 577–600. http://dx.doi.org/10.5194/gchron-4-577-2022.
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Abstract. Recent developments in tandem laser ablation mass spectrometer technology have demonstrated the capacity for separating parent and daughter isotopes of the same mass online. As a result, beta-decay chronometers can now be applied to the geological archive in situ as opposed to through traditional whole-rock digestions. One novel application of this technique is the in situ Rb–Sr dating of Proterozoic shales that are dominated by authigenic clays such as illite. This method can provide a depositional window for shales by differentiating signatures of early diagenetic processes versus late-stage secondary alteration. However, the hydrothermal sensitivity of the Rb–Sr isotopic system across geological timescales in shale-hosted clay minerals is not well understood. As such, we dated the Mesoproterozoic Velkerri Formation from the Altree 2 well in the Beetaloo Sub-basin (greater McArthur Basin), northern Australia, using this approach. We then constrained the thermal history of these units using common hydrocarbon maturity indicators and modelled effects of contact heating due to the intrusion of the Derim Derim Dolerite. In situ Rb–Sr dating of mature, oil-prone shales in the diagenetic zone from the Velkerri Formation yielded ages of 1448 ± 81, 1434 ± 19, and 1421 ± 139 Ma. These results agree with previous Re–Os dating of the unit and are interpreted as recording the timing of an early diagenetic event soon after deposition. Conversely, overmature, gas-prone shales in the anchizone sourced from deeper within the borehole were dated at 1322 ± 93 and 1336 ± 40 Ma. These ages are younger than the expected depositional window for the Velkerri Formation. Instead, they are consistent with the age of the Derim Derim Dolerite mafic intrusion intersected 800 m below the Velkerri Formation. Thermal modelling suggests that a single intrusion of 75 m thickness would have been capable of producing a significant hydrothermal perturbation radiating from the sill top. The intrusion width proposed by this model is consistent with similar Derim Derim Dolerite sill thicknesses found elsewhere in the McArthur Basin. The extent of the hydrothermal aureole induced by this intrusion coincides with the window in which kerogen from the Velkerri Formation becomes overmature. As a result, the mafic intrusion intersected here is interpreted to have caused kerogen in these shales to enter the gas window, induced fluids that mobilize trace elements, and reset the Rb–Sr chronometer. Consequently, we propose that the Rb–Sr chronometer in shales may be sensitive to temperatures of ca. 120 ∘C in hydrothermal reactions but can withstand temperatures of more than 190 ∘C in thermal systems not dominated by fluids. Importantly, this study demonstrates a framework for the combined use of in situ Rb–Sr dating and kerogen maturation indicators to help reveal the thermochronological history of Proterozoic sedimentary basins. As such, this approach can be a powerful tool for identifying the hydrocarbon potential of source rocks in similar geological settings.
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Veeranna, Thotakura, and Kiran Kumar Reddy. "Sliding window assisted mutual redundancy-based feature selection for intrusion detection system." International Journal of Ad Hoc and Ubiquitous Computing 40, no. 1/2/3 (2022): 176. http://dx.doi.org/10.1504/ijahuc.2022.123538.
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Veeranna, Thotakura, and Kiran Kumar Reddy. "Sliding window assisted mutual redundancy-based feature selection for intrusion detection system." International Journal of Ad Hoc and Ubiquitous Computing 40, no. 1/2/3 (2022): 176. http://dx.doi.org/10.1504/ijahuc.2022.10048197.
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Vutukuru, Krishna Sai, Mohammadtaghi Moravej, Amal Elawady, and Arindam Gan Chowdhury. "Holistic testing to determine quantitative wind-driven rain intrusion for shuttered and impact resistant windows." Journal of Wind Engineering and Industrial Aerodynamics 206 (November 2020): 104359. http://dx.doi.org/10.1016/j.jweia.2020.104359.
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Abuadlla, Yousef, Goran Kvascev, Slavko Gajin, and Zoran Jovanovic. "Flow-based anomaly intrusion detection system using two neural network stages." Computer Science and Information Systems 11, no. 2 (2014): 601–22. http://dx.doi.org/10.2298/csis130415035a.
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Computer systems and networks suffer due to rapid increase of attacks, and in order to keep them safe from malicious activities or policy violations, there is need for effective security monitoring systems, such as Intrusion Detection Systems (IDS). Many researchers concentrate their efforts on this area using different approaches to build reliable intrusion detection systems. Flow-based intrusion detection systems are one of these approaches that rely on aggregated flow statistics of network traffic. Their main advantages are host independence and usability on high speed networks, since the metrics may be collected by network device hardware or standalone probes. In this paper, an intrusion detection system using two neural network stages based on flow-data is proposed for detecting and classifying attacks in network traffic. The first stage detects significant changes in the traffic that could be a potential attack, while the second stage defines if there is a known attack and in that case classifies the type of attack. The first stage is crucial for selecting time windows where attacks, known or unknown, are more probable. Two different neural network structures have been used, multilayer and radial basis function networks, with the objective to compare performance, memory consumption and the time required for network training. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time, with low probability of false alarms.
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Mach’, Václav, Jan Valouch, and Milan Adámek. "Glass-break detector using accelerometer." MATEC Web of Conferences 210 (2018): 01003. http://dx.doi.org/10.1051/matecconf/201821001003.
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The aim of this paper is to summarize indoor detectors for peripheral intrusion detection. The most vulnerable part of the house are the windows which are fragile and very easy to overcome. The glass-break or shock detector can be used to detect the potential intruder. The goal of this paper also designs a glass-break detector which is based on the accelerometer. The accelerometer can be used as a detector which can detect the vibration in the glass panels. Final devices should be able to communicate with commercially made Control and Indicating Equipment.
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Dean, Mark T., and Nicholas Turner. "Conodont Colour Alteration Index (CAI) values for the Carboniferous of Scotland." Transactions of the Royal Society of Edinburgh: Earth Sciences 85, no. 3 (1994): 211–20. http://dx.doi.org/10.1017/s0263593300003606.
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AbstractConodont elements from Scottish Carboniferous rocks have been reviewed for Colour Alteration Index(CAI) data, and most values range between 1 and 1·5. Assuming a local average geothermal gradient similar to that of today, the observed and predicted CAI values generally fit well. Only a few of the samples analysed were influenced by local igneous intrusives. The CAI range shown lies within the immature (early dry gas) to mature (perhapsmid-oil window) stages of hydrocarbon generation, and this suggests that burial maturation (where CAI values are 1-5) could account for locally generated oil, where this occurred away from igneous intrusions. Alkali-dolerite and tholeiitic intrusives are, however, widespread in the Midland Valley of Scotland, and an understanding of their thermal effects has implications for both coal and petroleum exploration. The insensitivity of conodonts to low temperatures is noted, and the relevance, application and potential of various other palaeothermometers is discussed. Locally, the qualitative study of spore colour (SCI) appears useful.
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Rasha Thamer Shawe, Kawther Thabt Saleh, and Farah Neamah Abbas. "Building attack detection system base on machine learning." Global Journal of Engineering and Technology Advances 6, no. 2 (February 28, 2021): 018–32. http://dx.doi.org/10.30574/gjeta.2021.6.2.0010.
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These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.
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Qu, Hongchun, Libiao Lei, Xiaoming Tang, and Ping Wang. "A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks." Advances in Fuzzy Systems 2018 (June 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/4071851.
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For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.
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Čeponis, Dainius, and Nikolaj Goranin. "Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data." Applied Sciences 10, no. 7 (March 30, 2020): 2373. http://dx.doi.org/10.3390/app10072373.
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Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based on change monitoring of critical files and malware signatures, respectively, some recent research, utilizing relatively vanilla deep learning (DL) methods, has demonstrated promising anomaly-based detection results that already have practical applicability due low false positive rate (FPR). More complex DL methods typically provide better results in natural language processing and image recognition tasks. In this paper, we analyze applicability of more complex dual-flow DL methods, such as long short-term memory fully convolutional network (LSTM-FCN), gated recurrent unit (GRU)-FCN, and several others, for the task specified on the attack-caused Windows OS system calls traces dataset (AWSCTD) and compare it with vanilla single-flow convolutional neural network (CNN) models. The results obtained do not demonstrate any advantages of dual-flow models while processing univariate times series data and introducing unnecessary level of complexity, increasing training, and anomaly detection time, which is crucial in the intrusion containment process. On the other hand, the newly tested AWSCTD-CNN-static (S) single-flow model demonstrated three times better training and testing times, preserving the high detection accuracy.
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Baldini, Gianmarco. "On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks." Entropy 22, no. 9 (September 18, 2020): 1044. http://dx.doi.org/10.3390/e22091044.
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The evolution of modern automobiles to higher levels of connectivity and automatism has also increased the need to focus on the mitigation of potential cybersecurity risks. Researchers have proven in recent years that attacks on in-vehicle networks of automotive vehicles are possible and the research community has investigated various cybersecurity mitigation techniques and intrusion detection systems which can be adopted in the automotive sector. In comparison to conventional intrusion detection systems in large fixed networks and ICT infrastructures in general, in-vehicle systems have limited computing capabilities and other constraints related to data transfer and the management of cryptographic systems. In addition, it is important that attacks are detected in a short time-frame as cybersecurity attacks in vehicles can lead to safety hazards. This paper proposes an approach for intrusion detection of cybersecurity attacks in in-vehicle networks, which takes in consideration the constraints listed above. The approach is based on the application of an information entropy-based method based on a sliding window, which is quite efficient from time point of view, it does not require the implementation of complex cryptographic systems and it still provides a very high detection accuracy. Different entropy measures are used in the evaluation: Shannon Entropy, Renyi Entropy, Sample Entropy, Approximate Entropy, Permutation Entropy, Dispersion and Fuzzy Entropy. This paper evaluates the impact of the different hyperparameters present in the definition of entropy measures on a very large public data set of CAN-bus traffic with millions of CAN-bus messages with four different types of attacks: Denial of Service, Fuzzy Attack and two spoofing attacks related to RPM and Gear information. The sliding window approach in combination with entropy measures can detect attacks in a time-efficient way and with great accuracy for specific choices of the hyperparameters and entropy measures.
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Baldini, Gianmarco, Jose Luis Hernandez Ramos, and Irene Amerini. "Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy." Applied Sciences 11, no. 12 (June 16, 2021): 5567. http://dx.doi.org/10.3390/app11125567.
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The Intrusion Detection System (IDS) is an important tool to mitigate cybersecurity threats in an Information and Communication Technology (ICT) infrastructure. The function of the IDS is to detect an intrusion to an ICT system or network so that adequate countermeasures can be adopted. Desirable features of IDS are computing efficiency and high intrusion detection accuracy. This paper proposes a new anomaly detection algorithm for IDS, where a machine learning algorithm is applied to detect deviations from legitimate traffic, which may indicate an intrusion. To improve computing efficiency, a sliding window approach is applied where the analysis is applied on large sequences of network flows statistics. This paper proposes a novel approach based on the transformation of the network flows statistics to gray images on which Gray level Co-occurrence Matrix (GLCM) are applied together with an entropy measure recently proposed in literature: the 2D Dispersion Entropy. This approach is applied to the recently public IDS data set CIC-IDS2017. The results show that the proposed approach is competitive in comparison to other approaches proposed in literature on the same data set. The approach is applied to two attacks of the CIC-IDS2017 data set: DDoS and Port Scan achieving respectively an Error Rate of 0.0016 and 0.0048.
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Wu, Wufei, Yizhi Huang, Ryo Kurachi, Gang Zeng, Guoqi Xie, Renfa Li, and Keqin Li. "Sliding Window Optimized Information Entropy Analysis Method for Intrusion Detection on In-Vehicle Networks." IEEE Access 6 (2018): 45233–45. http://dx.doi.org/10.1109/access.2018.2865169.
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Feng, Zuhong, Jing Li, and Jie Hu Meichsner. "A Bayesian network intrusion detection algorithm based on principal component analysis and sliding window." International Journal of Security and Networks 9, no. 4 (2014): 216. http://dx.doi.org/10.1504/ijsn.2014.066177.
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Konyeha, Susan, and Emmanuel A. Onibere. "Computer Immunity Using an Intrusion Detection System (IDS)." Advanced Materials Research 824 (September 2013): 200–205. http://dx.doi.org/10.4028/www.scientific.net/amr.824.200.
Full textAbstract:
Computers are involved in every aspect of modern society and have become an essential part of our lives, but their vulnerability is of increasing concern to us. Security flaws are inherent in the operation of computers Most flaws are caused by errors in the process of software engineering or unforeseen mishaps and it is difficult to solve these problems by conventional methods. A radical way of constantly monitoring the system for newly disclosed vulnerabilities is required. In order to devise such a system, this work draws an analogy between computer immune systems and the human immune system. The computer immune system is the equivalent of the human immune system. The primary objective of this paper is to use an intrusion detection system in the design and implementation of a computer immune system that would be built on the framework of the human immune system. This objective is successfully realized and in addition a prevention mechanism using the windows IP Firewall feature has been incorporated. Hence the system is able to perform intrusion detection and prevention. Data was collected about events occurring in a computer network that violate predefined security policy, such as attempts to affect the confidentiality, integrity or its availability using Snort rules for known attacks and adaptive detection for the unknown attacks. The system was tested using real-time data and Intrusion Detection evaluation (IDEVAL) Department of Defense Advanced Research Projects Agency (DARPA) data set. The results were quite encouraging as few false positive were recorded.
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Liu, Zhenpeng, Nan Su, Yiwen Qin, Jiahuan Lu, and Xiaofei Li. "A Deep Random Forest Model on Spark for Network Intrusion Detection." Mobile Information Systems 2020 (December 22, 2020): 1–16. http://dx.doi.org/10.1155/2020/6633252.
Full textAbstract:
This paper focuses on an important research problem of cyberspace security. As an active defense technology, intrusion detection plays an important role in the field of network security. Traditional intrusion detection technologies have problems such as low accuracy, low detection efficiency, and time consuming. The shallow structure of machine learning has been unable to respond in time. To solve these problems, the deep learning-based method has been studied to improve intrusion detection. The advantage of deep learning is that it has a strong learning ability for features and can handle very complex data. Therefore, we propose a deep random forest-based network intrusion detection model. The first stage uses a slide window to segment original features into many small pieces and then trains a random forest to generate the concatenated class vector as rerepresentation. The vector will be used to train the multilevel cascade parallel random forest in the second stage. Finally, the classification of the original data is determined by voting strategy after the last layer of cascade. Meanwhile, the model is deployed in Spark environment and optimizes cache replacement strategy of RDDs by efficiency sorting and partition integrity check. The experiment results indicate that the proposed method can effectively detect anomaly network behaviors, with high F1-measure scores and high accuracy. The results also show that it can cut down the average execution time on different scaled clusters.