An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model

Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surface temperature and leaf area index could determine the dry and wet limits recognition schemes in grassland areas. The water vapor flux data of an eddy covariance system verified that the relative error between the ETd,METRIC and ETd,GES of eight DOYs (day of the year) was 18.8% on average. The ETMETRIC values of the crop growth season and the ETIMS of eight silage maize irrigation monitoring stations were found to have a relative error of 11.1% on average. The spatial distribution of the ET of the different land cover types in the study area was as follows: ETwater > ETarable land > ETforest land > ETunutilized land > ETgrassland > ETurban land.

Research of Metric Learning-Based Method for Person Re-Identification by Intelligent Computer Vision Technology

Person re-identification technology aims to establish an efficient metric model for similarity distance measurement of pedestrian images. Candidate images captured by different camera views are ranked according to their similarities to the target individual. However, the metric learning-based method, which is commonly used in similarity measurement, often failed in person re-identification tasks due to the drastic variations in appearance. The main reason for its low identification accuracy is that the metric learning method is over-fitting to the training data. Several types of metric learning methods which differ from each other by the distribution of sample pairs were summarized in this article for analysing and easing the metric learning methods’ over-fitting problem. Three different metric learning methods were tested on the VIPeR dataset. The distributions of the distance of the positive/negative training/test pairs are displayed to demonstrate the over-fitting problem. Then, a new metric model was proposed by combining the thoughts of binary classification and multi-class classification. Related verification experiments were conducted on VIPeR dataset. Besides, the semi-supervised metric learning approach was introduced to alleviate the over-fitting problem. The experimental results reflect gap between training pairs and test pairs in the metric subspace. Therefore, reducing the difference between training data and test data is a promising way to improve the identification accuracy of metric learning method.

KARAKTERISTIK PETIR INDONESIA DAN PENGGUNAANNYA DALAM EVALUASI UNJUK KERJA SALURAN UDARA 150 KV SAAT TERJADI SAMBARAN PETIR

Berdasarkan data historis, gangguan saluran transmisi udara di Indonesia mayoritas disebabkan oleh sambaran petir; hal tersebut mendorong PT PLN (Persero) sebagai perusahaan listrik milik negara untuk mengevaluasi unjuk kerja saluran udara terhadap sambaran petir. Pada makalah ini disajikan evaluasi karakterisasi petir menggunakan rekam data sistem deteksi petir/LDS (studi kasus untuk Jawa Barat) meliputi: jumlah sambaran, persentase polaritas, nilai modus arus puncak, persentase kejadian kumulatif, serta peta kerapatan petir. Evaluasi unjuk kerja saluran dilakukan melalui 1) simulasi tegangan lebih menggunakan perangkat lunak transient pada pemodelan saluran 150 kV untuk mengetahui korelasi arus puncak petir terhadap kenaikan tegangan pada insulator saat terjadi sambaran petir dan 2) evaluasi sudut lindung menggunakan Electro Geo-metric Model (EGM). Berdasarkan pengolahan data tahun 2018-2020, terdapat fluktuasi total kejadian petir per tahun dimana 84,63% (σ= 1,71) dari kejadian merupakan petir polaritas negatif. Modus nilai arus puncak petir adalah 12,33 kA (σ= 1,52), sementara persentase kejadian kumulatif memiliki knee point pada nilai 40 kA. Pola kerapatan petir tertinggi tidak mengalami perbedaan signifikan selama periode pengamatan dan bulan ke-7 merupakan periode dengan kejadian petir terendah. Hasil simulasi sambaran petir 40 kA pada kawat pembumiaan menunjukkan bahwa insulator mengalami kenaikan tegangan hingga 1083 kV; hal tersebut mendekati nilai Basic Impulse Insulation Level (BIL) dari insulator. Desain sudut lindung mampu mencegah shielding failure untuk arus petir ≥11 kA; meskipun demikian, perlu dicatat bahwa shielding failure akibat petir 10 kA menyebabkan kenaikan tegangan insulator melebihi nilai BIL. Peningkatan unjuk kerja saluran transmisi 150 kV tersebut dapat dilakukan dengan didasari oleh pertimbangan risk, cost dan benefit yang komprehensif.