Готові списки джерел за темами / Weighted adaptive average

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Добірка наукової літератури з теми "Weighted adaptive average"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 12 лютого 2022

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Статті в журналах з теми "Weighted adaptive average"

Capizzi, Giovanna, and Guido Masarotto. "An Adaptive Exponentially Weighted Moving Average Control Chart." Technometrics 45, no. 3 (August 2003): 199–207. http://dx.doi.org/10.1198/004017003000000023.

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Cao, Liqin, Lei Jiao, Zhijiang Li, Tingting Liu, and Yanfei Zhong. "Grayscale Image Colorization Using an Adaptive Weighted Average Method." Journal of Imaging Science and Technology 61, no. 6 (November 1, 2017): 605021–6050210. http://dx.doi.org/10.2352/j.imagingsci.technol.2017.61.6.060502.

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Mahmoud, Mahmoud A., and Alyaa R. Zahran. "A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart." Communications in Statistics - Theory and Methods 39, no. 4 (February 10, 2010): 606–25. http://dx.doi.org/10.1080/03610920902755813.

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Wang, Youqing, Xiangwei Wu, and Xue Mo. "A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction." Diabetes Technology & Therapeutics 15, no. 10 (October 2013): 792–801. http://dx.doi.org/10.1089/dia.2013.0104.

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HUBELE, NORMA FARIS, and SHING I. CHANG. "Adaptive Exponentially Weighted Moving Average Schemes Using a Kalrnan Filter." IIE Transactions 22, no. 4 (December 1990): 361–69. http://dx.doi.org/10.1080/07408179008964190.

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Huang, Wenpo, Lianjie Shu, and Yan Su. "An accurate evaluation of adaptive exponentially weighted moving average schemes." IIE Transactions 46, no. 5 (February 5, 2014): 457–69. http://dx.doi.org/10.1080/0740817x.2013.803642.

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HUANG, Kui. "TCP-Friendly Congestion Control Mechanism Based on Adaptive Weighted Average." Journal of Software 16, no. 12 (2005): 2124. http://dx.doi.org/10.1360/jos162124.

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Arshad, Asma, Muhammad Noor‐ul‐Amin, and Muhammad Hanif. "Function‐based adaptive exponentially weighted moving average dispersion control chart." Quality and Reliability Engineering International 37, no. 6 (April 20, 2021): 2685–98. http://dx.doi.org/10.1002/qre.2883.

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Galetto, Fernando J., Guang Deng, Mukhalad Al-Nasrawi, and Waseem Waheed. "Edge-Aware Filter Based on Adaptive Patch Variance Weighted Average." IEEE Access 9 (2021): 118291–306. http://dx.doi.org/10.1109/access.2021.3106907.

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Zheng, R., and S. Chakraborti. "A Phase II nonparametric adaptive exponentially weighted moving average control chart." Quality Engineering 28, no. 4 (July 14, 2016): 476–90. http://dx.doi.org/10.1080/08982112.2016.1183255.

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Більше джерел

Дисертації з теми "Weighted adaptive average"

Gan, Linmin. "Adaptive Threshold Method for Monitoring Rates in Public Health Surveillance." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/37721.

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Анотація:

We examine some of the methodologies implemented by the Centers for Disease Control and Preventionâ s (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of the W2r method designed using an empiric recurrence interval (RI) in this dissertation research. An adaptive threshold monitoring method is introduced based on fitting sample data to the underlying distributions, then converting the current value to a Z-score through a p-value. We compare the upper thresholds on the Z-scores required to obtain given values of the recurrence interval for different sets of parameter values. We then simulate one-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the power analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases, and the adaptive threshold method tends to outperform the W2 rate method. Two modified W2r methods proposed in the dissertation also tend to outperform the W2r method in terms of the RI threshold functions and in the power analysis.
Ph. D.

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Hellman, Hanna. "Data Aggregation in Time Sensitive Multi-Sensor Systems : Study and Implementation of Wheel Data Aggregation for Slip Detection in an Autonomous Vehicle Convoy." Thesis, KTH, Mekatronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217857.

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En övergång till bilar utrustade med avancerade automatiska säkerhetssystem (ADAS) och även utvecklingen mot självkörande fordon innebär ökad trafik på den lokala databussen. Det finns således ett behov av att både minska den faktiska mängden data som överförs, samtidigt som värdet på datat ökas. Data aggregation tillämpas i dagsläget inom områden såsom trådlösasensornätverk och mindre mobila robotar (WMR’s) och skulle kunna vara en del av en lösning. Denna rapport avser undersöka aggregation av sensordata i ett tidskänsligt system. För ett användarfall gällande halka under konvojkörning testas en aggregationsstrategi genom implementation på en fysisk demonstrator. Demonstratorn består av ett autonomt fordon i mindre skala som befinner sig i en konvoj med ett annat identiskt fordon. Resultaten pekar mot att ett viktat medelvärde, som i realtid anpassar sin viktning baserat på specifika sensorers koherens, med fördel kan användas för att estimera fordonshastighet baserat på individuella hjuls sensordata. Därefter kan en slip ratio beräknas, vilket avgör om fordonet befinner sig i ett tillstånd av halka eller ej. Begränsningar för den undersökta strategin inkluderar antalet icke-halkande hjul som behövs för tillförlitliga resultat. Simulerade resultat antyder att extra hastighetsreferenser behövs för tillförlitliga resultat. Relaterat till användarfallet konvojkörning föreslås att andra fordon används som hastighetsreferens. Detta skulle innebära en ökad precision för estimeringen av fordonshastigheten samt utgöra en intressant sammanslagning av områdena samarbetande cyberfysiska system (CO-CPS) och dataaggregation.
With an impending shift to more advanced safety systems and driver assistance (ADAS) in the vehicles we drive, and also increased autonomousity, comes increased amounts of data on the internal vehicle data bus. There is a need to lessen the amount of data and at the same time increase its value. Data aggregation, often applied in the field of environmental sensing or small mobile robots (WMR’s), could be a partial solution. This thesis choses to investigate an aggregation strategy applied to a use case regarding slip detection in a vehicle convoy. The approach was implemented in a physical demonstrator in the shape of a small autonomousvehicle convoy to produce quantitative data. The results imply that a weighted adaptive average can be used for vehicle velocity estimation based on the input of four individual wheel velocities. There after a slip ratio can be calculated which is used to decide if slip exists or not. Limitations of the proposed approach is however the number of velocity references that is needed since the results currently apply to one-wheel slipon a four-wheel vehicle. A proposed future direction related to the use case of convoy driving could be to include platooning vehicles as extra velocity references for the vehicles in the convoy, thus increasing the accuracy of the slip detection and merging the areas of CO-CPS and data aggregation.

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Tsai, Kai-Yuan, and 蔡開遠. "Frontalization and Adaptive Exponentially Weighted Average Ensemble Rule for Deep Learning Based Facial Expression Recognition." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q6w535.

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碩士
國立臺灣大學
電信工程學研究所
106
Nowadays, Automatic Facial Expression Recognition (FER) is an important technique in human-computer interfaces and surveillance systems, has attracted significant attention in pattern recognition and computer vision. Automatic systems for facial expression recognition receive the input (a static facial image or a facial image sequence) and classify it into one of the basic expressions (anger, sad, surprise, happy, disgust and fear, neutral and so on). Our work will focus on methods based on facial static images and it will consider the seven basic expressions. In this paper, we proposed a CNN based system with face frontalization and Hierarchical architecture for FER. The frontalized algorithm can align the small angle rotation (in-of-plane or out-of-plane) and use the face detection to remove the background noise, the adaptive exponentially weighted average ensemble rule can search the optimal weight according to the efficiency of classifier to improve the robust FER system. As a result, we perform the proposed system on some popular databases, the simulation results show that it is very effective for facial expression recognition, we achieve an accuracy rate surpassing the state-of-the-art system. Keyword: facial expression; convolutional neural networks; computer vision; face frontalization; hierarchical structure.

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Частини книг з теми "Weighted adaptive average"

Xu, Qing, Liang Ma, Weifang Nie, Peng Li, Jiawan Zhang, and Jizhou Sun. "Adaptive Fuzzy Weighted Average Filter for Synthesized Image." In Computational Science and Its Applications – ICCSA 2005, 292–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424857_32.

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Bui, Thi Mai Anh, and Nhat Hai Nguyen. "Adaptive Ranking Relevant Source Files for Bug Reports Using Genetic Algorithm." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210042.

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Precisely locating buggy files for a given bug report is a cumbersome and time-consuming task, particularly in a large-scale project with thousands of source files and bug reports. An efficient bug localization module is desirable to improve the productivity of the software maintenance phase. Many previous approaches rank source files according to their relevance to a given bug report based on simple lexical matching scores. However, the lexical mismatches between natural language expressions used to describe bug reports and technical terms of software source code might reduce the bug localization system’s accuracy. Incorporating domain knowledge through some features such as the semantic similarity, the fixing frequency of a source file, the code change history and similar bug reports is crucial to efficiently locating buggy files. In this paper, we propose a bug localization model, BugLocGA that leverages both lexical and semantic information as well as explores the relation between a bug report and a source file through some domain features. Given a bug report, we calculate the ranking score with every source files through a weighted sum of all features, where the weights are trained through a genetic algorithm with the aim of maximizing the performance of the bug localization model using two evaluation metrics: mean reciprocal rank (MRR) and mean average precision (MAP). The empirical results conducted on some widely-used open source software projects have showed that our model outperformed some state of the art approaches by effectively recommending relevant files where the bug should be fixed.

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Acerbi, Alberto. "Wary learners." In Cultural Evolution in the Digital Age, 21–48. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198835943.003.0002.

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Cultural evolution is a diverse field of research, but some similarities can be found: cultural evolutionists defend a quantitative, naturalistic, and interdisciplinary approach to the study of human culture. Importantly, cultural evolutionists are committed to develop sound hypotheses about the individual psychology that drives our cultural behavior. Although there are different nuances, a common idea is that human cognition is specialized for processing social interactions, communication, and learning from others. From an evolutionary point of view, the cognitive mechanisms involved should produce, on average, adaptive outcomes. From this perspective, social learning strategies (a series of relatively simple, general-domain, heuristics to choose when, what, and from whom to copy) provide a first boundary to indiscriminate social influence. I critically examine the concept of social learning strategies, and I discuss how cultural evolutionists may have overestimated both the effect of social influence and, possibly, our reliance of social learning itself. I also discuss the perspective from epistemic vigilance theory, which gives more weight to the possibility of explicit deception, and proposes that we apply sophisticated cognitive operations when deciding whether to trust information coming from others.

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Тези доповідей конференцій з теми "Weighted adaptive average"

Jie, Cao, and Xu Wei. "Research of Velocity Detection Based on the Adaptive Weighted Average Algorithm." In 2008 ISECS International Colloquium on Computing, Communication, Control, and Management. IEEE, 2008. http://dx.doi.org/10.1109/cccm.2008.351.

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Khaldi, Kais, Monia Turki-Hadj Alouane, and Abdel-Ouahab Boudraa. "Speech denoising by Adaptive Weighted Average filtering in the EMD framework." In 2008 2nd International Conference on Signals, Circuits and Systems (SCS). IEEE, 2008. http://dx.doi.org/10.1109/icscs.2008.4746884.

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Tsai, Kai-Yuan, Jian-Jiun Ding, and Yih-Cherng Lee. "Frontalization with Adaptive Exponentially-Weighted Average Ensemble Rule for Deep Learning Based Facial Expression Recognition." In 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2018. http://dx.doi.org/10.1109/apccas.2018.8605689.

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Kumar, S. Rakesh, K. Ramkumar, and Seshadhri Srinivasan. "Map spread factor based confidence weighted average technique for adaptive SLAM with unknown sensor model and noise covariance." In 2016 International Conference on Robotics: Current Trends and Future Challenges (RCTFC). IEEE, 2016. http://dx.doi.org/10.1109/rctfc.2016.7893405.

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Surender, Vellore P., and Ranjan Ganguli. "Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53080.

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The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter which is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal which simulates a single fault in gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their non-repeatability values which were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring as they can adapt to changes in quality of incoming data.

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Zhang, Xiang-Song, Wei-Xin Gao, and Shi-Ling Zhu. "Research on Noise Reduction and Enhancement of Weld Image." In 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101902.

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In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

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Dambrosio, L., S. M. Camporeale, and B. Fortunato. "Performance of Gas Turbine Power Plants Controlled by One Step Ahead Adaptive Technique." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0037.

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The One Step Ahead Controllers represent a branch of the Minimum Prediction Error Adaptive Controllers. They combine the parameter estimation of the controlled system model with a particular control scheme; therefore, they are especially suitable for non-linear and time-varying systems. Since the estimated parameters are updated at each time step (by using the sampled data), these methods can be adopted for real-time applications. Consequently, the One Step Ahead Controllers do not require the knowledge of the dynamic characteristics of the controlled system (e.g. state space systems or transfer functions). The One Step Ahead Adaptive (OSAA) algorithm combines the Least Square Algorithm (LSA) parameter estimator with a Deterministic Auto-Regressive Moving Average (DARMA) control scheme. The DARMA model can be characterized with a different number of time steps in the past (order of the estimated model) in relation to the dynamic feature of the controlled system. Sometimes, an excessive control effort could arise, caused by sudden variations of the electric load. In order to reduce this control action, the OSAA control technique has been applied also in Weighted fashion. The Weighted One Step Ahead Adaptive (WOSAA) control algorithm considers a penalty associated with the control effort by use an appropriate cost function. In this way, the control variable does not assume too large values, even when the Gas Turbine undergoes sudden changes in the external load. As a consequence, the robustness and the stability features of the WOSAA control system are increased with respect to the OSAA algorithm. The proposed techniques have been applied to a single shaft heavy-duty gas turbine (WOSAA) and to a double-shaft aero-derivative gas turbine (OSAA). They have been tested in Single-Input Single Output (SISO) mode. In the simulation tests, the plant is assumed to undergo sudden variations of the electric load. Second order schemes of the OSAA estimated model have been derived and applied to the double-shaft aero-derivative gas turbine. The results show that the OSAA control technique, applied to the double-shaft aero-derivative gas turbine, effectively counteracts the load reduction with limited overshoot in the controlled variables and, introducing an integral correction, with a negligible static error. On the other hand, the WOSAA control algorithm is able to efficiently regulate the single shaft heavy-duty gas turbine, and to counteract the sudden variations of the electric load, with reduced control effort.

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Zhang, Yi, Yucheng Sun, Qing Zhang, and Lu Yu. "Adaptive Weighted Averaged Template Matching Prediction for Intra Coding." In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018. http://dx.doi.org/10.1109/iscas.2018.8350997.

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Wang, Gou-Jen, Bor-Shin Lin, and Kang J. Chang. "Neural Network Based Run-to-Run Process Controller for Copper Chemical Mechanical Polishing." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59546.

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Process Control is one of the key methods to improve manufacturing quality. This research proposes a neural network based run-to-run process control scheme that is adaptive to the time-varying environment. Two multilayer feedforward neural networks are implemented to conduct the process control and system identification duties. The controller neural network equips the control system with more capability in handling complicate nonlinear processes. With the system information provided by this neural network, batch polishing time (T) an additional control variable, can be implemented along with the commonly used down force (p) and relative speed between the plashing pad and the plashed wafer (v). Computer simulations and experiments on copper chemical mechanical polishing processes illustrate that in drafting suppression and environmental changing adaptation that the proposed neural network based run-to-run controller (NNRTRC) performs better than the double exponentially weighted moving average (d-EWMA) approach. It is also suggested that the proposed approach can be further implemented as both an end-point detector and a pad-conditioning sensor.

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ŠENFELDE, Līga, and Daina KAIRIŠA. "AUTOMATIC CONCENTRATE DISTRIBUTION FOR FATTENING OF ROMANOV × DORPER LAMBS." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.062.

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The aim of this research was to study the possibility of using automatic concentrate feeding stations in fattening of lambs. Ten Romanov × Dorper weaned male lambs (initial live weight 21.0 ± 0.86 kg) for fattening were used. Lambs were kept indoors in separate pen and research was carried out in production conditions. Concentrate was distributed for animals individually in automatic feeding station. Adaption period were not applied, eight lambs had the concentrate intake in the automatic feeding station from first research day, one started eat concentrate from third research day and one – from eleventh day of research. The frequency of visits to automatic feeding station and daily concentrate intake was recorded and analyzed. Lamb’s were weighted before research and every fourteen days, live weight changes were analyzed. During all the research average number of daily visits to automatic feeding station of one lamb were 13 visits, average daily concentrate intake per animal was: 84 % of the average ration (1642 g) in all research period. Results shows, that average daily live weight gain was 246 ± 26.3 g, during last quarter daily live weight gain (89 ± 27.7 g) was significantly (p &amp;lt; 0.05) lover than in other quarters. For 1 kg lamb live weight gain 5.39 kg concentrate was used.

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