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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
dc.contributor.author | Wei-Sheng Chen | en |
dc.contributor.author | 陳韋勝 | zh_TW |
dc.date.accessioned | 2021-07-11T14:42:47Z | - |
dc.date.available | 2026-12-31 | |
dc.date.copyright | 2016-11-02 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-15 | |
dc.identifier.citation | Abrol, D. (1992). Foraging in honeybees Apis cerana indica F. and A. dorsata F. (Hymenoptera: Apidae)-activity and weather conditions. Journal of the Indian Institute of Science 72(5): 395.
Adeli, H., & Hung, S. L. (1995). Machine Learning: Neural Networks. Genetic Algorithms And Fuzzy Systems, John Wiley & Sons, Inc. Aikin, R. 1897. Bees evaporated: a new malady. Glngs. Bee Cult. 25: 479-480. Allan, S. A., Slessor, K. N., Winston, M. L., & King, G. G. S. (1987). The influence of age and task specialization on the production and perception of honey bee pheromones. Journal of insect physiology, 33(12), 917-922. Becher, M. A., Scharpenberg, H., & Moritz, R. F. (2009). Pupal developmental temperature and behavioral specialization of honeybee workers (Apis mellifera L.). Journal of Comparative Physiology A, 195(7), 673-679. Bergman, P., Molau, U., & Holmgren, B. (1996). Micrometeorological impacts on insect activity and plant reproductive success in an alpine environment, Swedish Lapland. Arctic and Alpine Research, 196-202. Buchmann, S. L., & Nabhan, G. P. (1997). The forgotten pollinators. Island Press. Bujok, Brigitte, et al. 'Hot spots in the bee hive.' Naturwissenschaften 89.7 (2002): 299-301. Chauvin, R., & Lavie, P. (1956, April). [Study of the antibiotic substance of pollen.]. In Annales de l'Institut Pasteur (Vol. 90, No. 4, pp. 523-527). Corbet, S. A., Fussell, M., Ake, R., Fraser, A., Gunson, C., Savage, A., & Smith, K. (1993). Temperature and the pollinating activity of social bees.Ecological Entomology, 18(1), 17-30. Cox-Foster, D. L., Conlan, S., Holmes, E. C., Palacios, G., Evans, J. D., Moran, N. A., ... & Lipkin, W. I. (2007). A metagenomic survey of microbes in honey bee colony collapse disorder. Science, 318(5848), 283-287. Cybenko, G. (1992). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS), 5(4), 455-455. Davis, G. A., & Nihan, N. L. (1991). Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117(2), 178-188. De Vito, S., Veneri, P. D., Esposito, E., Salvato, M., Bright, V., Jones, R. L., & Popoola, O. (2015, February). Dynamic multivariate regression for on-field calibration of high speed air quality chemical multi-sensor systems. InAISEM Annual Conference, 2015 XVIII (pp. 1-3). IEEE. Delaplane, K. S., Mayer, D. R., & Mayer, D. F. (2000). Crop pollination by bees. Cabi. Devillers, J. (Ed.). (2014). In silico bees. CRC Press. Dharia, A., & Adeli, H. (2003). Neural network model for rapid forecasting of freeway link travel time. Engineering Applications of Artificial Intelligence,16(7), 607-613. Diaconescu, E. (2008). The use of NARX neural networks to predict chaotic time series. Wseas Transactions on computer research, 3(3), 182-191. Erickson, E. H. (1975). Variability of floral characteristics influences honey bee visitation to soybean blossoms. Crop Science, 15(6), 767-771. Evans, J. D., Saegerman, C., Mullin, C., Haubruge, E., Nguyen, B. K., Frazier, M., ... & Pettis, J. S. (2009). Colony collapse disorder: a descriptive study. Fahrbach, S. E., & Robinson, G. E. (1995). Behavioral development in the honey bee: toward the study of. FAO. 2013. FAOSTAT online statistical service. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. Available at: http://faostat.fao.org/. Accessed: 10 October 2015. Florio, L., & Mussone, L. (1996). Neural-network models for classification and forecasting of freeway traffic flow stability. Control Engineering Practice, 4(2), 153-164 Free, J. B. 1967. Animal Behaviour, 15: 134-144. Free, J.B. 1993. Insect Pollination of Crops. San Diego, CA: Academic Press. Gary, N.E. (1992) Chapter 8. Activities and behavior of honey bees. In: Graham, J.M. (ed.) The hive and the honey bee, pp. 269–372. Dadant and Sons, Hamilton Gould J.L., Gould C.G. (1988) The Honey Bee. Scientific American Library. Hammer, M., & Menzel, R. (1995). Learning and memory in the honeybee. The Journal of Neuroscience, 15(3), 1617-1630. Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (1996). Neural network design (Vol. 20). Boston: PWS publishing company. Heinrich, B. (1993). The hot-blooded insects: strategies and mechanisms of thermoregulation. Harvard University Press. Heusner, A., & Stussi, T. (1964). Métabolisme énergétique de l'abeille isolée: son rôle dans la thermorégulation de la ruche. Insectes sociaux, 11(3), 239-265. Himmer, A. (1932). Die Temperaturverhältnisse bei den sozialen Hymenopteren. Biological Reviews, 7(3), 224-253. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. Jay, S. C. (1963). The development of honeybees in their cells. Journal of Apicultural Research, 2(2), 117-134. Jiang, J. A., Wang, C. H., Chen, C. H., Liao, M. S., Su, Y. L., Chen, W. S., ... & Chuang, C. L. (2016). A WSN-based automatic monitoring system for the foraging behavior of honey bees and environmental factors of beehives.Computers and Electronics in Agriculture, 123, 304-318. Jiang, X., & Adeli, H. (2005). Dynamic wavelet neural network model for traffic flow forecasting. Journal of transportation engineering, 131(10), 771-779. Jones, R. L., & Rothenbuhler, W. C. (1964). Behaviour genetics of nest cleaning in honey bees. II. Responses of two inbred lines to various amounts of cyanide-killed brood. Animal Behaviour, 12(4), 584-588. Kim, S., Park, S., & Kim, H. (2002). 'Method for controlling traffic flow using token bucket.' U.S. Patent Application No. 10/260,765. Koeniger, N. (1978). DAS WÄRMEN DER BRUT BEI DER HONIGBIENE (APIS MELLIFERA L.)*. Apidologie, 9(4), 305-320. Kremen, C., Williams, N. M., & Thorp, R. W. (2002). Crop pollination from native bees at risk from agricultural intensification. Proceedings of the National Academy of Sciences, 99(26), 16812-16816. Kwon, Y. J., & Saeed, S. (2003). Effect of temperature on the foraging activity of Bombus terrestris L.(Hymenoptera: Apidae) on greenhouse hot pepper (Capsicum annuum L.). Applied Entomology and Zoology, 38(3), 275-280. Lee, S., Fambro, D.B., (1999). Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Board 1678, 179–188 Lin, T. N., Giles, C. L., Horne, B. G., & Kung, S. Y. (1997). A delay damage model selection algorithm for NARX neural networks. IEEE Transactions on Signal Processing, 45(11), 2719-2730. Lindauer, M. 1952. Ein Beitrag zur Frage der Arbeitsteilung im Bienenstaat. Zeitschriftfür vergleichende Physiologie 34, 299–345. Lindauer, M. (1954). Temperaturregulierung und Wasserhaushalt im Bienenstaat. Zeitschrift für vergleichende Physiologie, 36(4), 391-432. Linksvayer, T. A., Fondrk, M. K., & Page Jr, R. E. (2009). Honeybee Social Regulatory Networks Are Shaped by Colony‐Level Selection. The American Naturalist, 173(3), E99-E107. Mattera, D., & Haykin, S. (1999, February). Support vector machines for dynamic reconstruction of a chaotic system. In Advances in kernel methods(pp. 211-241). MIT Press. McLellan, A.R. (1977) Honey bee colony weight as an index of honey production and nectar flow: a critical evaluation. J. Appl. Ecol. 14, 401–408 Okutani, I., & Stephanedes, Y. J. (1984). Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 18(1), 1-11. Oldroyd, B. P. (2007). What’s killing American honey bees. PLoS Biol, 5(6), e168. Park, B., Messer, C., & Urbanik II, T. (1998). Short-term freeway traffic volume forecasting using radial basis function neural network. Transportation Research Record: Journal of the Transportation Research Board, (1651), 39-47. Pham-Delegue, M.-H., Decourtye, A., Kaiser, L., Devillers, J. (2002) Behavioural methods to assess the effects of pesticides on honey bees. Apidologie 33, 425–432 Ratnieks, F. L., & Carreck, N. L. (2010). Clarity on honey bee collapse.Science, 327(5962), 152-153. Ross, P. (1982). Exponential filtering of traffic data (No. 869). Transportation Research Record, 869, pp. 43–49 Stabentheiner, A., Pressl, H., Papst, T., Hrassnigg, N., & Crailsheim, K. (2003). Endothermic heat production in honeybee winter clusters. Journal of Experimental Biology, 206(2), 353-358. Schacker, M. (2008). A spring without bees. Rowman & Littlefield. Seeley, T. D., & Visscher, P. (1985). Survival of honeybees in cold climates: the critical timing of colony growth and reproduction. Ecological Entomology, 10(1), 81-88. Seeley, T. D. 'The Wisdom of the HiveHarvard University Press.' Cambridge, MA (1995). Seeley, T. D. (2009). The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press. Smith, Brian L., and Michael J. Demetsky (1994). 'Short-term traffic flow prediction: neural network approach.' Transportation Research Record 1453. pp. 98–104 Smith, B. L., Williams, B. M., & Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting.Transportation Research Part C: Emerging Technologies, 10(4), 303-321. Smith, B. L., & Demetsky, M. J. (1997). Traffic flow forecasting: comparison of modeling approaches. Journal of transportation engineering, 123(4), 261-266. Southwick, E. E. (1985). Allometric relations, metabolism and heart conductance in clusters of honey bees at cool temperatures. Journal of Comparative Physiology B, 156(1), 143-149. Takens, F. (1981). Detecting strange attractors in turbulence. In Dynamical systems and turbulence, Warwick 1980 (pp. 366-381). Springer Berlin Heidelberg. Tautz, J., Maier, S., Groh, C., Rössler, W., & Brockmann, A. (2003). Behavioral performance in adult honey bees is influenced by the temperature experienced during their pupal development. Proceedings of the National Academy of Sciences, 100(12), 7343-7347. Tijani, I. B., Akmeliawati, R., Legowo, A., & Budiyono, A. (2014). Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution. Engineering Applications of Artificial Intelligence, 33, 99-115. Vanengelsdorp, D., R. Underwood, D. Caron, and J. Hayes Jr. 2007. An estimate of managed colony losses in the winter of 2006-2007: The Apiary Inspectors of America. American Bee Journal 147(7): 599-603. VanEngelsdorp, D., R. M. Underwood, and D. L. Cox-Foster. 2008. Short-term fumigation of honey bee (Hymenoptera: Apidae) colonies with formic and acetic acids for the control of Varroa destructor (Acari: Varroidae). Journal of Economic Entomology 101(2): 256-264. VanEngelsdorp, D., J. D. Evans, L. Donovall, C. Mullin, M. Frazier, J. Frazier, D. R. Tarpy, J. Hayes Jr, and J. S. Pettis. 2009. “Entombed Pollen”: A new condition in honey bee colonies associated with increased risk of colony mortality. Journal of invertebrate pathology 101(2): 147-149. VanEngelsdorp, D., and M. D. Meixner. 2010. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. Journal of invertebrate pathology 103: S80-S95. Van Langevelde, F., & Jaarsma, C. F. (2004). Using traffic flow theory to model traffic mortality in mammals. Landscape ecology, 19(8), 895-907. Williams, B., Durvasula, P., & Brown, D. (1998). Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record: Journal of the Transportation Research Board, (1644), 132-141. Williams, G. R., D. R. Tarpy, D. Vanengelsdorp, M. P. Chauzat, D. L. Cox‐Foster, K. S. Delaplane, P. Neumann, J. S. Pettis, R. E. Rogers and D. Shutler. 2010. Colony collapse disorder in context. Bioessays 32(10): 845-846. Yasdi, R. (1999). Prediction of road traffic using a neural network approach.Neural computing & applications, 8(2), 135-142. Yun, S. Y., Namkoong, S., Rho, J. H., Shin, S. W., & Choi, J. U. (1998). A performance evaluation of neural network models in traffic volume forecasting. Mathematical and Computer Modelling, 27(9), 293-310. Zacepins, A., Brusbardis, V., Meitalovs, J., & Stalidzans, E. (2015). Challenges in the development of Precision Beekeeping. Biosystems Engineering, 130, 60-71. Zhang, J., Chen, Y. P., & Marsic, I. (2008, March). Network coding via opportunistic forwarding in wireless mesh networks. In 2008 IEEE Wireless Communications and Networking Conference (pp. 1775-1780). IEEE. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78118 | - |
dc.description.abstract | 蜜蜂是自然界中最重要的植物授粉者,目前人類的食物有三分之一來自於開花植物,其中約有百分之八十需要蜜蜂協助授粉,此外地球上還有許多植物需要蜜蜂扮演傳媒的角色,故蜜蜂對於人類的農作物生產,及地球生態系平衡的影響力,占有舉足輕重的地位。近年來,世界各地陸續發現大量蜜蜂不明消失的現象,使得蜂農損失慘重,根據相關研究人員研究發現,大量蜂群消失之原因為工蜂無法回巢而凋亡,此現象稱為蜂群崩潰失調症 。
本研究欲開發一蜂群行為預測模型,其主要目地為針對蜂群每日之活動力進行預測,本研究首先研製一蜂群行為即時監測系統,此系統目的為監測蜂群每日出入蜂箱之行為,系統亦能自動化記錄當下之環境溫濕度與蜂箱內溫濕度;後端分析部分,所提出之方法為使用外部輸入非線性自動回歸模型之時間稽延神經網路 ,並透過輸入環境溫度參數及蜂群入巢頻率參數訓練模型;本研究採用移動視窗的方式下,研究時間將規劃成五個移動視窗,移動時間的長度為四週。此外,於每個移動視窗內之資料各自分為樣本內配適資料(訓練資料)及樣本外預測資料(測試資料)。本研究針對每個視窗內樣本內資料分別配適出適當之稽延神經網路預測模型,並評估模型建立後對樣本外資料的預測績效。至於針對模型的預測績效,本文分別以預測誤差及均方誤差來做比較,且本研究所提出之預測模型其預測誤差約在15%,故模型之準確率約為85%。 本研究之貢獻在於開發一蜂群行為預測模型,此模型將可提供蜂農及研究人員精準且客觀的預測數據,透過預測數據將可在蜂群之蜂勢開始崩潰或凋亡前進行防範及補救措施。 | zh_TW |
dc.description.abstract | Honey bees play an important role in pollinating flowering plants. According to the survey, one third of the world’s food supply originates from flowering plants, about 80% require the assistance from bees for pollination. Thus honeybees have played an important role in crop production and have had a crucial influence in the world's ecological balance. However, in recent years, many countries around the world have witnessed a mysterious phenomenon that honeybee populations disappear one after another, causing a serious loss to beekeepers. According to related research, the majority of bee colonies have collapsed due to worker bees withering to death after being unable to locate their nests. It is a particular case of collapse of honey bee colonies and still unresolved.
The purpose of this study is to develop a prediction model for honeybee flight behavior by using time-delay neural network (TDNN) forecasting. To achieve this goal, this study first develop the real-time honeybee behavior monitoring system. In order to quantify the number and the frequency of out-going and in-coming bees. This research develops a time-delay neural network with NARX (nonlinear autoregressive network with exogenous inputs) dynamic neural architecture. The effectiveness, feasibility and robustness of the proposed method are demonstrated on a real data set, which is the historical honey bee in-coming frequency data and ambient temperature data measured from bee counter. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns of daily honey bee flight activity among process variables. The percentage error of the prediction result is less than 15%. That is the accurate of this neural network model is reached up to 85%. The prediction result helps beekeeper and researchers to have a better understanding of forager flight behavior and takes actions before colony collapse. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:42:47Z (GMT). No. of bitstreams: 1 ntu-105-R03631035-1.pdf: 2926104 bytes, checksum: e956c56522f67929e39c8037bdb6290d (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | Table of Contents
List of Illustrations vi List of Tables ix Chapter 1 Introduction 1 1.1. Background 1 1.2. Motivation 4 1.3. Purpose 6 1.4. Thesis Organization 7 Chapter 2 Literature Review 8 2.1. Colony Collapse Disorder 8 2.2. The Flight Behavior of Honey bees 12 2.3. Prediction Models for Flight Behavior 15 2.4. Theorem of the NARX neural network 20 2.5. Neural Network Model Comparison 24 Chapter 3 Materials and Methods 27 3.1. Research Target 27 3.2. The Architecture of the Honey bee Behavior Monitoring System 29 3.3. Data Set 37 3.4. Time delay neural network 38 3.4.1. Neural network selection 38 3.4.2. NARX neural networks 40 3.4.3. Function approximation 41 3.4.4. Tapped delay lines 42 3.4.5. Number of Hidden Neurons 44 3.4.6. Number of layers 45 Chapter 4 Results and Discussion 48 4.1. Time Delay Neural Network 48 4.2. The results in the training phase 49 4.3. The Prediction Results 58 Chapter 5 Conclusion 67 References 71 | |
dc.language.iso | en | |
dc.title | 時間稽延神經網路應用於蜂群預測之研究 | zh_TW |
dc.title | A Time Delay Neural Network on the Prediction of Honey Bee Colony Activity | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 楊恩誠(En-Cheng Yang),艾群(Chyung Ay),謝建興(Jiann-Shing Shieh) | |
dc.subject.keyword | 蜂群出入巢行為,時間稽延神經網路,外部輸入自動回歸模型,蜂群行為監測系統, | zh_TW |
dc.subject.keyword | Honey bee flight behavior,time delay neural network,NARX model,bee counter, | en |
dc.relation.page | 81 | |
dc.identifier.doi | 10.6342/NTU201602511 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-16 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
dc.date.embargo-lift | 2026-12-31 | - |
顯示於系所單位: | 生物機電工程學系 |
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