Model Routing Data pada Pembangkit Listrik Tenaga Mikrohidro (PLTMH) Berbasis Jaringan Sensor Nikabel (JSN)

Pembangkit listrik tenaga mikrohidro adalah salah satu sumber energi listrik terbarukan saat ini dengan memanfaatkan tenaga air berskala kecil untuk penggeraknya. PLTMH pada umumnya memiliki ruang kontrol yang berada jauh dengan sistemnya, sehingga dibutuhkan suatu sistem monitoring. Penggunaan sistem monitoring berbasis nirkabel menjadi salah satu solusi untuk mengatasi masalah tersebut dengan proses pengiriman informasi yang cepat dan mudah. Penelitian ini merancang suatu model routing data dengan perancangan hardware dan software pada komunikasi data. Sistem dibangun menggunakan mikrokontroller arduino dan komunikasi frekuensi Xbee dengan protocol zigbee. Pengujian dilakukan dengan menggunakan berbagai macam topologi yaitu topologi pair, star, mesh, tree, dan x dalam model routing data baik dalam lingkungan indoor maupun lingkungan outdoor. Hasil yang didapat berdasarkan pengujian pada lingkungan indoor menunjukan Xbee tetap dapat mengirim dalam ruangan yang memiliki penghalang dengan jarak 21m, sedangkan pada pengujian outdoor, Xbee dapat tetap mengirim dengan jarak maksimum 120.5m menggunakan model topologi x. Berdasarkan semua pemodelan yang telah dilakukan, topologi x merupakan model yang dianggap paling berhasil walaupun memiliki kelemahan dalam tingkat kerapihan penerimaan data namun dari tingkat keamanannya ketika terjadi masalah dengan salah satu node, topologi x masih memiliki jalur lain untuk dapat mengirimkan data ke penerima. Micro hydropower plant is one of the current renewable electrical energy by making use of small-scale water power for propulsion. MHPP generally has a control room that was away with the system, so we need a monitoring system. The use of wireless-based monitoring systems into one solution to overcome these problems with the delivery process information quickly and easily. Therefore, we designed a model of routing data by designing hardware and software in data communication comprising Arduino microcontroller and Xbee frequency communication with ZigBee protocol. Testing is done by using a variety of topologies, namely topology pair, star, mesh, tree, and x in the model routing of data in both the indoor and outdoor environment. The results obtained by testing the indoor environment is Xbee can still send in a room that had a barrier at a distance of 21m, while the outdoor testing Xbee can still send more than the maximum distance delivery Xbee 70.5m in research that is by using the model topology x. Based on all the modeling that has been done, topology x is the model that is considered the most successful even though it has weaknesses in the level of data reception tidiness. But the level of security has the advantage that when there is a problem with one of the nodes, the x topology still has another path to be able to send data to the receiver.

MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

MotivationEffectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify.ResultsIn this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network [email protected]