https://www.econstor.eu/bitstream/10419/244042/1/1694103390.pdf

Review on sun tracking technology in solar PV system

Pertumbuhan ekonomi Indonesia yang diukur berdasarkan kenaikan Produk Domestik Bruto (PDB) pada triwulan II 2008 mencapai 2,4 persen dibanding triwulan I 2008 (q to q) dan apabila dibandingkan dengan triwulan yang sama tahun 2007 mengalami pertumbuhan 6,4 persen (y on y). 
Secara kumulatif, pertumbuhan ekonomi Indonesia semester I-2008 dibandingkan dengan semester I-2007 sebesar 6,4 persen. 
Besaran PDB atas dasar harga berlaku pada triwulan II 2008 mencapai Rp1.230,9 triliun, sedangkan PDB atas dasar harga konstan 2000 pada triwulan yang sama adalah Rp518,2 triliun. 
Tiga sektor yang mengalami pertumbuhan tertinggi (q to q) adalah sektor pertanian 5,1 persen; sektor pengangkutan dan komunikasi 4,1 persen; dan sektor listrik, gas dan air bersih 3,6 persen. Sementara untuk pertumbuhan (y on y) sektor pengangkutan dan komunikasi tumbuh 19,6 persen; sektor listrik, gas dan air bersih 11,2 persen; dan sektor keuangan, real estat dan jasa perusahaan 8,7 persen. 
Struktur PDB triwulan II 2008 masih didominasi oleh sektor industri pengolahan; sektor pertanian; dan sektor perdagangan, hotel dan restoran masing-masing memberikan kontribusi sebesar 27,3 persen, 14,7 persen dan 14,3 persen. 
Pengeluaran konsumsi rumah tangga pada triwulan II-2008 dibandingkan dengan triwulan I-2008 (q to q) meningkat sebesar 1,1 persen. Sementara pengeluaran konsumsi pemerintah meningkat 21,2 persen, pembentukan modal tetap bruto 2,4 persen, ekspor barang dan jasa 5,2 persen, serta impor barang dan jasa 5,4 persen. 
Dibandingkan dengan triwulan II 2007 (y on y), pengeluaran konsumsi rumah tangga meningkat 5,3 persen, pengeluaran konsumsi pemerintah 2,2 persen, pembentukan modal tetap bruto 12,8 persen, ekspor barang dan jasa 16,1 persen, serta impor barang dan jasa 16,7 persen. 􀀻 Struktur perekonomian Indonesia secara spasial masih didominasi oleh Pulau Jawa yang memberikan kontribusi terhadap PDB Nasional sebesar 57,5 persen kemudian diikuti oleh Pulau Sumatera sebesar 24,2 persen, Pulau Kalimantan 9,9 persen dan Pulau Sulawesi 4,1 persen serta sisanya sebesar 4,3 persen.

Pertumbuhan ekonomi indonesia triwulan ii-2008

The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

/pdf/a-novel-deep-learning-method-for-detection-and-52ymf95otz.pdf

A novel deep learning method for detection and classification of plant diseases

This paper presents a methodology for enhancement of panorama images environment by calculating high dynamic range. Panorama is constructing by merge of several photographs that are capturing by traditional cameras at different exposure times. Traditional cameras usually have much lower dynamic range compared to the high dynamic range in the real panorama environment, where the images are captured with traditional cameras will have regions that are too bright or too dark. A more details will be visible in bright regions with a lower exposure time and more details will be visible in dark regions with a higher exposure time. Since the details in both bright and dark regions cannot preserve in the images that are creating using traditional cameras, the proposed system have to calculate one using the images that traditional camera can actually produce. The proposed systems start by get LDR panorama image from multiple LDR images using SIFT features technology and then convert this LDR panorama image to the HDR panorama image using inverted local patterns. The results in this paper explained that the HDR panorama images that resulting from the proposed method is more realistic image and appears as it is a real panorama environment.

https://www.beei.org/index.php/EEI/article/download/2362/1735

Generation of high dynamic range for enhancing the panorama environment

This paper presents a thorough review of state-of-the-art research and literature in the field of photovoltaic tracking systems for the production of electrical energy. A review of the literature is performed mainly for the field of solar photovoltaic tracking systems, which gives this paper the necessary foundation. Solar systems can be roughly divided into three fields: the generation of thermal energy (solar collectors), the generation of electrical energy (photovoltaic systems), and the generation of electrical energy/thermal energy (hybrid systems). The development of photovoltaic systems began in the mid-19th century, followed shortly by research in the field of tracking systems. With the development of tracking systems, different types of tracking systems, drives, designs, and tracking strategies were also defined. This paper presents a comprehensive overview of photovoltaic tracking systems, as well as the latest studies that have been done in recent years. The review will be supplemented with a factual presentation of the tracking systems used at the Institute of Energy Technology of the University of Maribor.

https://www.mdpi.com/1996-1073/13/16/4224/pdf

Solar Photovoltaic Tracking Systems for Electricity Generation: A Review

Dual-axis sun tracker sensor based on tetrahedron geometry

Monitoring the output parameters of a solar module is required for assessing its performance under real operating conditions. This paper presents a new technique for monitoring the output parameters i.e. current and voltage of solar module directly under real operating conditions. In this new monitoring technique, the output parameters of a solar module can be directly processed in real time condition and their results are displayed in a graph. The monitoring system is developed using microprocessor Arduino Atmega 328P and equipped with calibrated current and voltage sensors, a data acquisition system which is integrated directly into an Excel spreadsheet using the PLX-DAQ application program and a memory card for backup. The monitoring system is connected to a computer using a RS232 serial port. The collected data is saved directly into a spreadsheet and plotted in real time. This technique provides an easy access to the collected data for further analysis.

/pdf/pemantauan-parameter-panel-surya-berbasis-arduino-secara-ng02awrl3i.pdf

Pemantauan Parameter Panel Surya Berbasis Arduino secara Real Time

Early detection of plant diseases is one of the main keys to handling diseases quickly and successfully. The purpose of this study is to find out a simpler CNN architecture and meet an acceptable compromise between accuracy and simplification to detect diseases in tomato plants based on leaf images. This simpler architecture will allow the development of standalone and independent system model in the field to classify and identify the tomato plants diseases in low price and limited resources. This proposed architecture was developed from the CNN architecture baseline and is intended to classify 10 classes of tomato leaves consist of one healthy class and 9 classes of leaves diseases taken from the Plant Village dataset. In this study, the performance of the proposed architecture and comparative architectures are examined in the same dataset. Comparative architectures used are some existing CNN architectures that are commonly used namely VGG Net, Shuffle Net and Squeeze Net. The results indicated that the proposed architecture can achieve competitive accuracy compared with the existing architecture while the proposed architecture is much shorter than the existing architecture and better in terms of performance time.

https://robots.iopscience.iop.org/article/10.1088/1742-6596/1845/1/012009/pdf

Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images

In recent years, cross spectral matching has been gaining attention in various biometric systems for identification and verification purposes. Cross spectral matching allows images taken under different electromagnetic spectrums to match each other. In cross spectral matching, one of the keys for successful matching is determined by the features used for representing an image. Therefore, the feature extraction step becomes an essential task. Researchers have improved matching accuracy by developing robust features. This paper presents most commonly selected features used in cross spectral matching. This survey covers basic concepts of cross spectral matching, visual and thermal features extraction, and state of the art descriptors. In the end, this paper provides a description of better feature selection methods in cross spectral matching.

http://journal.portalgaruda.org/index.php/EEI/article/download/843/1043

Yuwaldi Away

Papers

Dual-axis sun tracker sensor based on tetrahedron geometry

Pemantauan Parameter Panel Surya Berbasis Arduino secara Real Time

Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images

Features for Cross Spectral Image Matching: A Survey

Effective and fast binarization method for combined degradation on ancient documents.