基于SOA的水轮机调速系统PID参数优化

针对现有优化方法复杂、易陷入局部最优等问题,为水轮机调速系统提出了一种基于人群搜索算法的比例-积分-微分控制参数优化方法.为验证该算法的可行性,建立了水轮机调速系统非线性模型,选取水轮发电机组转速偏差的积分时间绝对误差指标作为目标函数进行优化.为验证优化结果的有效性,将人群搜索算法的控制效果与参考文献中粒子群算法的控制效果进行了对比分析.仿真结果表明,在5%频率扰动下,人群搜索算法自第29次迭代起已收敛,经其优化的系统能在8秒内趋于稳定,此时系统的超调量为1.6%;在10%负荷扰动下,人群搜索算法自第25次迭代起已收敛,其优化效果与粒子群算法优化效果基本相同,两者均在10秒内让系统趋于稳定,但人群搜索算法优化的积分时间绝对误差指标比粒子群算法优化的积分时间绝对误差指标小,表明人群搜索算法具有更好的搜索功能,在一定程度上改善了孤网运行条件下机组的动态性能.     

高能湿法球磨制备鱼胶原肽螯合钙及其理化特征

为了拓展胶原肽螯合钙的制备工艺,以鱼胶原肽和CaCl2为原料,以高能湿法球磨的方法制备了鱼胶原肽螯合钙,并采用FT-IR红外图谱、X-衍射、EDS能谱等技术手段对其理化特性和结构进行分析。结果显示,随着球磨时间从15 min延长到240 min,鱼胶原肽的平均粒径逐渐降低,ZETA电位绝对值、分子量和pH值逐渐增加,鱼胶原肽对钙的螯合率逐渐从74.21%增加到85.42%。鱼胶原肽螯合钙与鱼胶原肽的主体化学结构相似,但是球磨后鱼胶原肽的-COO-1 VAS吸收峰(1 643.89 cm~(-1))向短波数方向移动到1 540～1 555 cm~(-1),而-NH2的特征吸收峰(3 313.9 cm~(-1))向长波数方向移动到3 337.12～3 380.59 cm~(-1)。球磨后鱼胶原在2θ=20°处的特征宽吸收峰消失,而在2θ=26、28、48和58°处出现多个尖峰和弥散峰,表明其从无定形结构向兼具结晶和无定形结构转变。鱼胶原肽螯合钙中钙元素比例逐渐增加,证实鱼胶原肽对钙的螯合率增加。研究表明,高能湿法球磨可应用于鱼胶原肽螯合钙的制备,该方法具有加工工艺简单、无污染和螯合率高等优点。     

鄱阳湖区反嘴鹬种群数量动态与空间分布格局

2002—2015年,采用同步调查法对鄱阳湖区反嘴鹬(Recurvirostra avosetta)越冬种群数量动态与空间分布格局进行了监测。结果表明:鄱阳湖区分布有稳定的反嘴鹬越冬种群,年平均数量为(16684±14653)只,2004年、2005年、2013年种群数量均超过目前其全球数量的10%,2004年冬季鄱阳湖区越冬反嘴鹬数量达到最大值,为47520只。鄱阳湖区共有70个湖泊记录到反嘴鹬,其中反嘴鹬种群数量达到全球1%以上的湖泊有20个。每年冬季鄱阳湖区反嘴鹬集中分布在常湖、蚌湖、花庙湖和下坝湖,而反嘴鹬利用频次最高的是大湖池,每年反嘴鹬的越冬数量为(1819±1593)只。各湖泊中反嘴鹬的年平均数量与利用频次之间不存在显著相关性。自然保护区涵盖了反嘴鹬在鄱阳湖越冬的主要湖泊,分布在保护区内的反嘴鹬种群占鄱阳湖区越冬总数量的(75.3±26.0)%,但仍有很多重要的湖泊没有被纳入到保护区范围。反嘴鹬对保护区内湖泊的利用频次与保护区外湖泊的利用频次不存在显著性差异,保护区内反嘴鹬的种群数量显著大于保护区外。     

金乌贼雄性规格和社群数量对求偶与交配行为的影响

为探讨不同规格雄性金乌贼(Sepia esculenta)在求偶交配过程中的竞争及优势等级,采用实验生态学方法,在室内可控条件下分别设置1L1S组、1L2S组和1L4S组(L和S分别表示大规格雄性和小规格雄性,数字表示实验中雄性金乌贼数量),连续摄像观察和记录金乌贼的繁殖行为。结果显示:(1)繁殖过程中金乌贼具有明显的领域行为和护卫伴游行为。(2)随着群体中小规格雄性比例的增加,处于优势地位的大规格雄性个体的优势等级发生变化,主要表现为大规格雄性护卫伴游时间、成功交配次数逐渐减少,而小规格雄性护卫伴游时间、成功交配次数逐渐增多。(3)当群体中小规格雄性较少时,大规格雄性常常主动攻击小规格雄性,而当小规格雄性数量远大于大规格雄性时,小规格雄性主动向其他雄性发起攻击,以争取护卫权和交配权。研究表明,雄性金乌贼的规格和数量对于求偶竞争以及交配行为具有显著影响。随着繁殖群体中小规格雄性增多,大规格雄性成功交配次数逐渐降低。1L4S组成功交配次数稍高于1L2S组,显著高于1L1S组(P0.05)。人工苗种繁育过程中选择合适规格的雄性亲体和合理的雌雄比例,对提高金乌贼繁殖效率具有重要意义。     

基于粒子群算法优化BP神经网络的土壤含水量短期预测模型

为提高土壤含水量预测精度,基于物联网监测数据,提出了粒子群算法(PSO)优化BP神经网络的土壤含水量预测方法。首先应用主成分分析法筛选出影响土壤含水量的关键影响因子,然后构建8-5-1的BP神经网络拓扑结构,应用粒子群算法优化BP神经网络的初始权值和阈值。结果表明:与传统BP神经网络相比,新模型优化了网络结构,避免了陷入局部最优解,具有良好的预测效果;模型的评价指标平均绝对误差、平均绝对百分误差、误差均方根分别为0.259 2、0.010 5和0.135 6,与单一BP神经网络相比,预测精度更高,可满足实际的土壤含水量预测的需要。     

Three SNPs within exons of INHA and ACVR2B genes are significantly associated with litter size in Dazu black goats

The aim of this study was to analyse the association between single-nucleotide polymorphisms within INHA and ACVR2B and litter size in Dazu black goats. In total, twenty-two SNPs were genotyped in 190 individuals by SNaPshot and resequencing. The results showed that three SNPs (SNP_1, SNP_12 and SNP_13 in this study) were detected to have significant additive genetic effect on the recorded goat litter size (p < .05). The SNP_1 (NC_030809.1), a non-synonymous substitution of G for T at chr2-g. 28314990 in the exon 2 of INHA gene (NM_001285606.1), resulted in homozygote 2 (HOM2) contributed 0.25 and heterozygote (HET) contributed 0.12 larger litter than homozygote 1 (HOM1). Meanwhile, SNP_12 (Chr22-g. 11721225 A > T) and SNP_13 (Chr22-g. 11721227 A > C) (NC_030829.1) simultaneously mutated at the first and third position of a triplet AAA (lysine, K) in the exon 4 of ACVR2B gene (XM_018066623.1) had estimated genetic effects of HOM1 (0.00) and HOM2 (0.03) larger than HET (−0.12). In conclusion, one SNPs (chr2-g. 28314990 T > G) within the exon 2 of INHA and two SNPs (Chr22-g. 11721225 A > T and Chr22-g. 11721227 A > C) i n the exon 4 of ACVR2B gene were highly recommended as candidate markers of litter size in Dazu black goats. A large-scale association study to assess the impact of these variants on litter size is still necessary.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Diet,size and location as determinants of n‐3 long‐chain polyunsaturated fatty acid content in farmed Atlantic Salmon (Salmo salar)

We explored how currently manufactured feeds, under real‐world conditions and across geographically distinct locations, promoted flesh n‐3 long‐chain polyunsaturated fatty acid (LC‐PUFA, i.e. 20:5n‐3 + 22:6n‐3) levels in various life stages of farmed Atlantic Salmon (Salmo salar). Potential effects on flesh LC‐PUFA included: (1) diet and fish weight at one Canadian east coast farm, (2) diet and farm location across six east coast farms, and (3) diet and farm location between east and west coast farms. For objectives 1 and 2, salmon were fed a currently manufactured feed (labelled as feeds A, B or C) and harvested at 1, 3 and 5 kg. LC‐PUFA levels in 5 kg (harvest size) fish were then compared to previously published values for west coast farmed Atlantic Salmon (Obj. 3). Combined results revealed that variability in LC‐PUFA levels was better explained by diet than by fish weight or farm location. Fish size, however, was also important for two reasons. First, feeding a high LC‐PUFA diet early in life appeared important for ensuring high LC‐PUFA levels at harvest size. Second, salmon flesh LC‐PUFA levels increased with fish size, but only when dietary LC‐PUFA was provided above an apparent threshold value (~3000 mg per 100 g or 10% of total fatty acids) that likely promoted LC‐PUFA incorporation and storage. Overall, our comparison makes new recommendations for feed manufacturers and demonstrates that farmed Atlantic Salmon reared under real‐world conditions on currently available salmon feeds were good sources of n‐3 LC‐PUFA to consumers.     

Kernel vitreousness and protein content: Relationship,interaction and synergistic effects on durum wheat quality

Four sets of durum samples were used in this study to further understand the interrelationships among hard vitreous kernels (HVK), protein content, and pigment concentration, with a focus on the interaction and synergistic effects of protein content and vitreousness on durum quality. HVK level increases with higher protein content in the range of 9.5–12.5%, but this relationship is less evident in durum samples with high protein content (12.5–14.5%). Both protein content and kernel vitreousness can significantly affect durum milling quality. White starchy kernels (WSK) in low protein durum have a very detrimental impact on milling and pasta processing quality, but high protein content can mitigate the adverse impact of WSK on durum quality. Although protein content plays a dominant role, higher HVK might contribute positively to pasta firmness. There was no significant difference in yellow pigment content between HVK and WSK. However, pigment loss from semolina to dough was higher for WSK than HVK. Despite the difference in protein content, HVK and WSK have little difference in gluten strength. The monomeric protein was preferentially accumulated in HVK. The glutenin proteins of HVK and WSK were similar in the ratios of 1Bx/1By and HMW/LMW-GS.