Variable precision rough set (VPRS) is an extension of rough set theory (RST). By setting threshold value β , VPRS looses the strict definition of approximate boundary in RST. Confident threshold value for β is disc...Variable precision rough set (VPRS) is an extension of rough set theory (RST). By setting threshold value β , VPRS looses the strict definition of approximate boundary in RST. Confident threshold value for β is discussed and the method for deriving decision making rules from an information system is given by an example. An approach to fuzzy measures of knowledge is proposed by applying VPRS to fuzzy sets. Some properties of this measure are studied and a pair of lower and upper approximation operato...展开更多
It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotat...It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.展开更多
In the case of Z＋^d（d ≥ 2）-the positive d-dimensional lattice points with partial ordering ≤, {Xk,k∈ Z＋^d} i.i.d, random variables with mean 0, Sn =∑k≤nXk and Vn^2 = ∑j≤nXj^2, the precise asymptotics for ∑...In the case of Z＋^d（d ≥ 2）-the positive d-dimensional lattice points with partial ordering ≤, {Xk,k∈ Z＋^d} i.i.d, random variables with mean 0, Sn =∑k≤nXk and Vn^2 = ∑j≤nXj^2, the precise asymptotics for ∑n1/｜n｜（log｜n｜dP（｜Sn/Vn｜≥ε√log log｜n｜） and ∑n（logn｜）b/｜n｜（log｜n｜）^d-1P（｜Sn/Vn｜≥ε√log n）,as ε↓0,is established.展开更多
Recent increases in irrigated hectares in the Southeastern US have enabled growers to obtain higher yields through applying nutrients through irrigation water. Therefore, many growers apply nutrients through irrigatio...Recent increases in irrigated hectares in the Southeastern US have enabled growers to obtain higher yields through applying nutrients through irrigation water. Therefore, many growers apply nutrients through irrigation systems, known as fertigation. Currently, there are no practical decision-making tools available for variable-rate application of nitrogen (N) through overhead sprinkler irrigation systems. Therefore, field tests were conducted on cotton (Gossypium hirsutum L.) during the 2016 and 2017 growing seasons to 1) adapt the Clemson sensor-based N recommendation algorithms from a single side-dress application to multiple applications through an overhead irrigation system;and 2) to compare sensor-based VRFS with conventional nutrient management methods in terms of N use efficiency (NUE) and crop responses on three soil types. Two seasons of testing Clemson N prediction algorithms to apply multiple applications of N were very promising. The multiple applications of N compared to the grower’s conventional methods (even though less N was applied) had no impact on yields in either growing season. There was no difference in cotton yields between 101 and 135 kg/ha N applications in either management zone. Also, there were no differences in yield between sensor-based, multiple N applications and conventional N management techniques. In relation to comparisons of the sensor methods only applying N in three or four applications, statistically increased yields compared to single or split applications in 2016. Applying N in four applications, statistically increased yields compared to single, split or triple applications in 2017. When the sensor-based methods were compared to the grower’s conventional methods averaged over four treatments, the sensor-based N applications reduced fertilizer requirement by 69% in 2016 and 57% in 2017 compared to grower’s conventional methods. When comparing N rates among the four sensor-based methods (three or four) applications, increased N rates by 22 kg/ha in 2016 and 26 kg/ha in 2017 compared to single or split applications but increased the cotton lint yields by 272 and 139 kg/ha, for 2016 and 2017, respectively.展开更多
Nutrients are injected through overhead irrigation systems at a uniform rate in a process known as fertigation. The highly variable soils in the Southeastern US pose challenges for effective fertigation. Currently, th...Nutrients are injected through overhead irrigation systems at a uniform rate in a process known as fertigation. The highly variable soils in the Southeastern US pose challenges for effective fertigation. Currently, there is no variable-rate fertigation system available to apply the correct amount of N within a field through an overhead irrigation system. Therefore, the objective of this study was to develop and test a variable-rate N application system that works independently of irrigation water flow for site-specific N application. The variable-rate fertigation system (VRFS) was designed to apply different rates N using a pulse width modulation technique. The VRFS utilized the Clemson Lateral Irrigation Control software which controlled the solenoids in each zone by turning the N supply on and off (pulsing) for each zone. In this study, four tests were conducted to determine the uniformity of the VRFS. In test # 1, the pump output showed a linear slope relationship and was the same for water and N. In test # 2, nozzle flow and uniformity were determined using four different irrigation system travel speeds at N application rates of 31, 59, 88, and 113 kg/ha. There was a strong correlation (R2 = 0.9998) between irrigation system speed and N rate. In test # 3, the uniformity across the length of the irrigation system was determined. The nozzles produced an average flow of 31.1, 58.7, 87.6, and 112.7 kg N/ha with an overall average error of 0.1% across all N rates. Results also showed the system was capable of accurately applying N based on prescription maps with an error of less than 1.8%. Test # 4 was conducted to determine the accuracy of the map-based controller system for applying variable rate N. There was a strong correlation between target N and actual N rates (R2 = 0.9999). In summary, the VRFS applied the correct amounts of N within each zone by either manually controlling the pulsing mechanism or utilizing a prescription map to apply different rates throughout the field.展开更多
A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confide...A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA problem with multiple decision attributes and multiple condition attributes. Empirical results show that the decision rule with the highest confidence measures will be used as the final decision rules in the MADA problem with multiple conflicting decision attributes and multiple condition attributes if there are some conflicts among decision rules resulting from multiple decision attributes. The confidence-measure-based VPRS model can effectively solve the conflicts of decision rules from multiple decision attributes and thus a class of MADA problem with multiple conflicting decision attributes and multiple condition attributes are solved.展开更多
Variability in soil properties is a critical element across wide areas of researches especially in several aspects of agriculture and environment including sewage disposal and global climate change. Particle size frac...Variability in soil properties is a critical element across wide areas of researches especially in several aspects of agriculture and environment including sewage disposal and global climate change. Particle size fraction (sand, silt, and clay), effective cation exchange capacity, base saturation, pH, organic carbon, total nitrogen, carbon nitrogen ratio, available phosphorus, exchangeable bases (calcium, magnesium, sodium, potassium) and acidity are frequently used in agriculture for soil management. The objective of this study therefore was to identify soil management factors from these set of 15 soil properties and spatial distribution of representative soil management properties. The study was carried out in the University of Uyo Teaching and Research Farm measuring 8.19 hectares in University of Uyo Annex, Uyo in Akwa Ibom State of Nigeria. Nine and ten traverses were made horizontally and vertically respectively at 40 meters intervals. A total of 58 soil samples were collected at 0 - 15 cm depth on the grid nodes of the traverses. Particle size distributions, exchangeable bases and acidity, effective cation exchange capacity (ECEC), available phosphorus (avail. P), base saturation (BS), organic carbon, total nitrogen, carbon nitrogen ratio (CNR) and pH of the samples were determined in the laboratory. Coefficient of variation indicated that 26.6% of the soil properties (sand content, pH, CNR and sodium) were least variable, 40.1% comprising silt, clay contents, ECEC, base saturation, phosphorus and magnesium were moderately. Whereas 33.3% of the soil properties comprising clay content, organic carbon, total nitrogen, exchangeable Ca, K and acidity (i.e.) were highly variable. There were significant correlation (p < 0.05) in 26.6% of the soil properties, the strongest negative significant (p < 0.01) correlations were between sand and clay (r = –0.85), exchangeable acidity and base saturation (r = –0.85), whereas the strongest positive significant correlations were between ECEC and Ca (r = 0.80), Ca and BS (r = 0.74), organic carbon and total nitrogen (r = 0.80). Principal component analysis indicated the existence of six factors including mineralogical or weathering, soil organic matter, cation exchange activity, soil texture, and dispersion and soil phosphorus based on either management or pedological considerations. Semivariance statistics showed that sand and clay contents, ECEC, BS and total N were moderately (≥25.7% ≤47.3%), while silt content, pH, organic carbon, CNR, avail. P, exchangeable Ca, Mg, Na and acidity (≥0.18% ≤22.8%) were strongly spatially dependent. The variability observed was primarily incident upon factors of soil formation. Therefore, the utilization of spatial structure of organic matter and texture factors in the management of nutrient and soil water will facilitate planning of crop production scheme on coastal plain sands soils.展开更多
This paper is an extension of Hanif, Hamad and Shahbaz estimator [1] for two-phase sampling. The aim of this paper is to develop a regression type estimator with two auxiliary variables for two-phase sampling when we ...This paper is an extension of Hanif, Hamad and Shahbaz estimator [1] for two-phase sampling. The aim of this paper is to develop a regression type estimator with two auxiliary variables for two-phase sampling when we don’t have any type of information about auxiliary variables at population level. To avoid multi-collinearity, it is assumed that both auxiliary variables have minimum correlation. Mean square error and bias of proposed estimator in two-phase sampling is derived. Mean square error of proposed estimator shows an improvement over other well known estimators under the same case.展开更多
Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, ...Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, the rate of VRT adoption is very low here. Hence, analyzing the factors influencing the adoption and providing a regional estimate of the impact of VRT adoption on cotton yield is very important. This study used the 2009 Southern Cotton Precision Farming Survey to analyze the farm and farmer characteristics affecting the adoption of VRT among Texas cotton farmers and to empirically estimate the impact of adoption of VRT on cotton yield in Texas. A two-stage least square procedure with a logistic regression model in the first stage and a multiple linear regression model in the second stage was used to analyze the data. The study revealed that there are significant regional differences in adoption pattern within the state of Texas; and the farmers from the coastal region, where there is higher within-field variability, were more likely to adopt VRT compared to other regions. Younger farmers, farmers managing larger farms, and farmers who use computers for farming operations were more likely to adopt VRT. The results also showed that, on an average, the adoption of VRT does not lead to significant yield improvements for cotton in Texas. Since the impact of VRT adoption on yield is not significant, the source of economic advantage of VRT adoption in Texas may be the reduction of input cost.展开更多
This paper describes the field tests carried out to evaluate the new Vicon RS-EDW centrifugal fertilizer spreader, to be used in Precision Agriculture. The results of initial dynamic calibration tests showed a signifi...This paper describes the field tests carried out to evaluate the new Vicon RS-EDW centrifugal fertilizer spreader, to be used in Precision Agriculture. The results of initial dynamic calibration tests showed a significant and systematic difference between the application rates of the two discs （about 20%）. This difference had to be corrected by the manufacturer that carried out the necessary changes to the electric actuators. The fertilizer spreader had good distribution homogeneity, considering different working velocities and different positions in the parcel. The pattern distribution curve for 18% super phosphate fertilizer led to an effective working width of 28 meters, with a coefficient of variation of 15%. The longitudinal test, under experimental working conditions led to a machine delay time of 6-7 seconds. The results show an actual fertilization application density between 74%-90% of that determined for each location. These results confirm that the spreader can be used to spread fertilizer differentially in the field.展开更多
By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variable...By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variables. Firstly, the spatial space and temporal domain are discretized by FEM and precise integral algorithm respectively. Then, the high accuracy semi-analytical solution of direct problem can be got. Finally, based on the solution, the computing model of inverse problem and expression of sensitivity analysis are established. Single variable and variables combined identifications including thermal parameters, boundary conditions and source-related terms etc. are given to validate the approach proposed in 1-D and 2-D cases. The effects of noise data and initial guess on the results are investigated. The numerical examples show the effectiveness of this approach.展开更多
Cotton root growth is often hindered in the Southeastern U.S. due to the presence of root-restricting soil layers. Tillage must be used to temporarily remove this compacted soil layer to allow root growth to depths ne...Cotton root growth is often hindered in the Southeastern U.S. due to the presence of root-restricting soil layers. Tillage must be used to temporarily remove this compacted soil layer to allow root growth to depths needed to sustain plants during periods of drought. However, the use of a uniform depth of tillage may be an inefficient use of energy due to the varying depth of this root-restricting layer. Therefore, the objective of this project was to develop and test equipment for controlling tillage depth “on-the-go” to match the soil physical parameters, and to determine the effects of site-specific tillage on soil physical properties, energy requirements, and plant responses in cotton production. Site-specific tillage operations reduced fuel consumption by 45% compared to conventional constant-depth tillage. Only 20% of the test field required tillage at recommended depth of 38-cm deep for Coastal Plain soils. Cotton taproot length in the variable-depth tillage plots was 96% longer than those in the no-till plots (39 vs. 19.8 cm). Statistically, there was no difference in cotton lint yield between conventional and the variable-depth tillage. Deep tillage (conventional or variable-rate) increased cotton lint yields by 20% compared to no-till.展开更多
文摘Variable precision rough set (VPRS) is an extension of rough set theory (RST). By setting threshold value β , VPRS looses the strict definition of approximate boundary in RST. Confident threshold value for β is discussed and the method for deriving decision making rules from an information system is given by an example. An approach to fuzzy measures of knowledge is proposed by applying VPRS to fuzzy sets. Some properties of this measure are studied and a pair of lower and upper approximation operato...
基金the support of the National Natural Science Foundation of China under Grant No.60673023,60433020,10501017,3040016the European Commission for TH/Asia Link/010 under Grant No.111084.
文摘It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.
文摘In the case of Z＋^d（d ≥ 2）-the positive d-dimensional lattice points with partial ordering ≤, {Xk,k∈ Z＋^d} i.i.d, random variables with mean 0, Sn =∑k≤nXk and Vn^2 = ∑j≤nXj^2, the precise asymptotics for ∑n1/｜n｜（log｜n｜dP（｜Sn/Vn｜≥ε√log log｜n｜） and ∑n（logn｜）b/｜n｜（log｜n｜）^d-1P（｜Sn/Vn｜≥ε√log n）,as ε↓0,is established.
文摘Recent increases in irrigated hectares in the Southeastern US have enabled growers to obtain higher yields through applying nutrients through irrigation water. Therefore, many growers apply nutrients through irrigation systems, known as fertigation. Currently, there are no practical decision-making tools available for variable-rate application of nitrogen (N) through overhead sprinkler irrigation systems. Therefore, field tests were conducted on cotton (Gossypium hirsutum L.) during the 2016 and 2017 growing seasons to 1) adapt the Clemson sensor-based N recommendation algorithms from a single side-dress application to multiple applications through an overhead irrigation system;and 2) to compare sensor-based VRFS with conventional nutrient management methods in terms of N use efficiency (NUE) and crop responses on three soil types. Two seasons of testing Clemson N prediction algorithms to apply multiple applications of N were very promising. The multiple applications of N compared to the grower’s conventional methods (even though less N was applied) had no impact on yields in either growing season. There was no difference in cotton yields between 101 and 135 kg/ha N applications in either management zone. Also, there were no differences in yield between sensor-based, multiple N applications and conventional N management techniques. In relation to comparisons of the sensor methods only applying N in three or four applications, statistically increased yields compared to single or split applications in 2016. Applying N in four applications, statistically increased yields compared to single, split or triple applications in 2017. When the sensor-based methods were compared to the grower’s conventional methods averaged over four treatments, the sensor-based N applications reduced fertilizer requirement by 69% in 2016 and 57% in 2017 compared to grower’s conventional methods. When comparing N rates among the four sensor-based methods (three or four) applications, increased N rates by 22 kg/ha in 2016 and 26 kg/ha in 2017 compared to single or split applications but increased the cotton lint yields by 272 and 139 kg/ha, for 2016 and 2017, respectively.
文摘Nutrients are injected through overhead irrigation systems at a uniform rate in a process known as fertigation. The highly variable soils in the Southeastern US pose challenges for effective fertigation. Currently, there is no variable-rate fertigation system available to apply the correct amount of N within a field through an overhead irrigation system. Therefore, the objective of this study was to develop and test a variable-rate N application system that works independently of irrigation water flow for site-specific N application. The variable-rate fertigation system (VRFS) was designed to apply different rates N using a pulse width modulation technique. The VRFS utilized the Clemson Lateral Irrigation Control software which controlled the solenoids in each zone by turning the N supply on and off (pulsing) for each zone. In this study, four tests were conducted to determine the uniformity of the VRFS. In test # 1, the pump output showed a linear slope relationship and was the same for water and N. In test # 2, nozzle flow and uniformity were determined using four different irrigation system travel speeds at N application rates of 31, 59, 88, and 113 kg/ha. There was a strong correlation (R2 = 0.9998) between irrigation system speed and N rate. In test # 3, the uniformity across the length of the irrigation system was determined. The nozzles produced an average flow of 31.1, 58.7, 87.6, and 112.7 kg N/ha with an overall average error of 0.1% across all N rates. Results also showed the system was capable of accurately applying N based on prescription maps with an error of less than 1.8%. Test # 4 was conducted to determine the accuracy of the map-based controller system for applying variable rate N. There was a strong correlation between target N and actual N rates (R2 = 0.9999). In summary, the VRFS applied the correct amounts of N within each zone by either manually controlling the pulsing mechanism or utilizing a prescription map to apply different rates throughout the field.
基金The National Natural Science Foundation of China (No.70221001)the Knowledge Innovation Program of Chinese Academyof Sciences (No.3547600)Strategy Research Grant of City University of Hong Kong (No.7001677)
文摘A variable precision rough set (VPRS) model is used to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes. By introducing confidence measures and a β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving the MADA problem with multiple decision attributes and multiple condition attributes. Empirical results show that the decision rule with the highest confidence measures will be used as the final decision rules in the MADA problem with multiple conflicting decision attributes and multiple condition attributes if there are some conflicts among decision rules resulting from multiple decision attributes. The confidence-measure-based VPRS model can effectively solve the conflicts of decision rules from multiple decision attributes and thus a class of MADA problem with multiple conflicting decision attributes and multiple condition attributes are solved.
文摘Variability in soil properties is a critical element across wide areas of researches especially in several aspects of agriculture and environment including sewage disposal and global climate change. Particle size fraction (sand, silt, and clay), effective cation exchange capacity, base saturation, pH, organic carbon, total nitrogen, carbon nitrogen ratio, available phosphorus, exchangeable bases (calcium, magnesium, sodium, potassium) and acidity are frequently used in agriculture for soil management. The objective of this study therefore was to identify soil management factors from these set of 15 soil properties and spatial distribution of representative soil management properties. The study was carried out in the University of Uyo Teaching and Research Farm measuring 8.19 hectares in University of Uyo Annex, Uyo in Akwa Ibom State of Nigeria. Nine and ten traverses were made horizontally and vertically respectively at 40 meters intervals. A total of 58 soil samples were collected at 0 - 15 cm depth on the grid nodes of the traverses. Particle size distributions, exchangeable bases and acidity, effective cation exchange capacity (ECEC), available phosphorus (avail. P), base saturation (BS), organic carbon, total nitrogen, carbon nitrogen ratio (CNR) and pH of the samples were determined in the laboratory. Coefficient of variation indicated that 26.6% of the soil properties (sand content, pH, CNR and sodium) were least variable, 40.1% comprising silt, clay contents, ECEC, base saturation, phosphorus and magnesium were moderately. Whereas 33.3% of the soil properties comprising clay content, organic carbon, total nitrogen, exchangeable Ca, K and acidity (i.e.) were highly variable. There were significant correlation (p < 0.05) in 26.6% of the soil properties, the strongest negative significant (p < 0.01) correlations were between sand and clay (r = –0.85), exchangeable acidity and base saturation (r = –0.85), whereas the strongest positive significant correlations were between ECEC and Ca (r = 0.80), Ca and BS (r = 0.74), organic carbon and total nitrogen (r = 0.80). Principal component analysis indicated the existence of six factors including mineralogical or weathering, soil organic matter, cation exchange activity, soil texture, and dispersion and soil phosphorus based on either management or pedological considerations. Semivariance statistics showed that sand and clay contents, ECEC, BS and total N were moderately (≥25.7% ≤47.3%), while silt content, pH, organic carbon, CNR, avail. P, exchangeable Ca, Mg, Na and acidity (≥0.18% ≤22.8%) were strongly spatially dependent. The variability observed was primarily incident upon factors of soil formation. Therefore, the utilization of spatial structure of organic matter and texture factors in the management of nutrient and soil water will facilitate planning of crop production scheme on coastal plain sands soils.
文摘This paper is an extension of Hanif, Hamad and Shahbaz estimator [1] for two-phase sampling. The aim of this paper is to develop a regression type estimator with two auxiliary variables for two-phase sampling when we don’t have any type of information about auxiliary variables at population level. To avoid multi-collinearity, it is assumed that both auxiliary variables have minimum correlation. Mean square error and bias of proposed estimator in two-phase sampling is derived. Mean square error of proposed estimator shows an improvement over other well known estimators under the same case.
文摘Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, the rate of VRT adoption is very low here. Hence, analyzing the factors influencing the adoption and providing a regional estimate of the impact of VRT adoption on cotton yield is very important. This study used the 2009 Southern Cotton Precision Farming Survey to analyze the farm and farmer characteristics affecting the adoption of VRT among Texas cotton farmers and to empirically estimate the impact of adoption of VRT on cotton yield in Texas. A two-stage least square procedure with a logistic regression model in the first stage and a multiple linear regression model in the second stage was used to analyze the data. The study revealed that there are significant regional differences in adoption pattern within the state of Texas; and the farmers from the coastal region, where there is higher within-field variability, were more likely to adopt VRT compared to other regions. Younger farmers, farmers managing larger farms, and farmers who use computers for farming operations were more likely to adopt VRT. The results also showed that, on an average, the adoption of VRT does not lead to significant yield improvements for cotton in Texas. Since the impact of VRT adoption on yield is not significant, the source of economic advantage of VRT adoption in Texas may be the reduction of input cost.
文摘This paper describes the field tests carried out to evaluate the new Vicon RS-EDW centrifugal fertilizer spreader, to be used in Precision Agriculture. The results of initial dynamic calibration tests showed a significant and systematic difference between the application rates of the two discs （about 20%）. This difference had to be corrected by the manufacturer that carried out the necessary changes to the electric actuators. The fertilizer spreader had good distribution homogeneity, considering different working velocities and different positions in the parcel. The pattern distribution curve for 18% super phosphate fertilizer led to an effective working width of 28 meters, with a coefficient of variation of 15%. The longitudinal test, under experimental working conditions led to a machine delay time of 6-7 seconds. The results show an actual fertilization application density between 74%-90% of that determined for each location. These results confirm that the spreader can be used to spread fertilizer differentially in the field.
文摘By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variables. Firstly, the spatial space and temporal domain are discretized by FEM and precise integral algorithm respectively. Then, the high accuracy semi-analytical solution of direct problem can be got. Finally, based on the solution, the computing model of inverse problem and expression of sensitivity analysis are established. Single variable and variables combined identifications including thermal parameters, boundary conditions and source-related terms etc. are given to validate the approach proposed in 1-D and 2-D cases. The effects of noise data and initial guess on the results are investigated. The numerical examples show the effectiveness of this approach.
文摘Cotton root growth is often hindered in the Southeastern U.S. due to the presence of root-restricting soil layers. Tillage must be used to temporarily remove this compacted soil layer to allow root growth to depths needed to sustain plants during periods of drought. However, the use of a uniform depth of tillage may be an inefficient use of energy due to the varying depth of this root-restricting layer. Therefore, the objective of this project was to develop and test equipment for controlling tillage depth “on-the-go” to match the soil physical parameters, and to determine the effects of site-specific tillage on soil physical properties, energy requirements, and plant responses in cotton production. Site-specific tillage operations reduced fuel consumption by 45% compared to conventional constant-depth tillage. Only 20% of the test field required tillage at recommended depth of 38-cm deep for Coastal Plain soils. Cotton taproot length in the variable-depth tillage plots was 96% longer than those in the no-till plots (39 vs. 19.8 cm). Statistically, there was no difference in cotton lint yield between conventional and the variable-depth tillage. Deep tillage (conventional or variable-rate) increased cotton lint yields by 20% compared to no-till.
