Perubahan Pola Iklim dan Pengaruhnya terhadap Waktu Panen Duku (Lansium domesticum Corr.)

Sugiarto A, Tanjung RD, Pratama R 2022. Changes in climate patterns and their effects on harvest time of duku (Lansium domesticum Corr.). In: Herlinda S et al. (Eds.), Prosiding Seminar Nasional Lahan Suboptimal ke-10 Tahun 2022, Palembang 27 Oktober 2022. pp. 859-870. Palembang: Penerbit & Percetakan Universitas Sriwijaya (UNSRI).Climatic factors are essential in the fruit formation and development process of seasonal fruits, such as duku (Lansium domesticum Corr.). This study aimed to determine changes in climate patterns and their relation to the harvest time of duku. This research takes climate study data from 2018 and 2021, the processing and analysis of data to see climate patterns. Observation the harvest time of duku took a case study on plantations located in three villages (Berkat, Kijang Awal Terusan, and Serdang Menang), Sirah Pulau Padang District, Ogan Komering Ilir Regency. The results show that the climate conditions in 2018 and 2021 are not much different, but the climate patterns are very different. The harvest time under climatic conditions in 2018 is in January-February 2019, while the harvest time under climatic conditions in 2021 is in October-November 2021, January 2022, and March 2022. Observation of the harvest time in three villages under climatic conditions in 2018 looks the same, but the harvest time for 2021 climatic conditions is different. The harvest time in Berkat Village is in October and January, Kijang Awal Terusan in January and March, and Serdang Menang in November, January, and March. These results indicate that changes in climate patterns will affect the harvest time of duku

Generative deep fields : arbitrarily sized, random synthetic astronomical images through deep learning

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond.Peer reviewe

Neuron-level fuzzy memoization in RNNs

The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, each recurrent layer is executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we make the observation that the output of a neuron exhibits small changes in consecutive invocations. We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches the output of each neuron and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 24.2% of computations, resulting in 18.5% energy savings and 1.35x speedup on average.Peer ReviewedPostprint (author's final draft

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85