A user oriented microcomputer facility for designing linear quadratic Gaussian feedback compensators

A laboratory design facility for digital microprocessor implementation of linear-quadratic-Gaussian feedback compensators is described. Outputs from user interactive programs for solving infinite time horizon LQ regulator and Kalman filter problems were conditioned for implementation on the laboratory microcomputer system. The software consisted of two parts: an offline high-level program for solving the LQ Ricatti equations and generating associated feedback and filter gains and a cross compiler/macro assembler which generates object code for the target microprocessor system. A PDP 11/70 with a UNIX operating system was used for all high level program and data management, and the target microprocessor system is an Intel MDS (8080-based processor). Application to the control of a two dimensional inverted pendulum is presented and issues in expanding the design/prototyping system to other target machine architectures are discussed

Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach

This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.Comment: This paper has been accepted at the 2023 Deep Learning for Precision Agriculture (DLSPA) Workshop, at The 2023 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD

Ajaran Moral dalam Lirik Lagu Dolanan Anak

Lagu dolanan anak adalah salah satu bentuk lagu jawa yang digunakan anak-anak dari suku jawa untuk bermain. Artikel ini bertujuan untuk menjelaskan ajaran moral dalam lirik lagu dolanan anak tersebut. Hal yang akan dibahas yaitu mengenai wujud, jenis dan cara menjabarkan ajaran moral dalam lagu dolanan anak. metode penelitian yang digunakan dalam penelitian ini adalah deskriptif. Data penelitian ini berwujud kata, frasa, dan perkataan dalam lirik lagu dolanan. Data penelitian ini diperoleh dengan cara membaca berkali-kali lirik lagu dolanan anak dilanjutkan dengan menulis data. Selanjutnya data dengan cara deskrip- tif, untuk menemukan wujud, jenis, dan cara menjabarkan ajaran moralnya

NetSim: The framework for complex network generator

Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks

How to predict social relationships — Physics-inspired approach to link prediction

© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton's Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision–Recall Curve (AUC)for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network's global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction

The Fabrication Of Metal-Oxide Semiconductor Transistors Using Cerium Dioxide As A Gate Oxide Material

Cerium dioxide was employed as a gate insulator for an enhancement-type n-channel metal-oxide-semiconductor (MOS) transistor. Cerium was evaporated in a tungsten boat and immediately oxidized for oxide uniformity. The use of CeO2 as a gate oxide in MOS transistor yielded a low positive threshold voltage with negligible interface charge effects. This resulted in the transistor performing as an enhancement type device

Rancang Bangun Alat Pasang Surut Air Laut Berbasis Arduino Uno dengan Menggunakan Sensor Ultrasonik Hc-sr04

Telah dirancang sistem pasang surut air laut berbasis Arduino Uno dengan menggunakan sensor ultrasonik HC-SR04. Sensor ultrasonik berfungsi mengukur ketinggian air laut. Tampilan dari sistem ini berupa ketinggian air laut sesaat yang ditampilkan pada LCD. Selain itu hasil dari sistem ini juga berupa grafik pasang surut yang dirancang menggunakan software Delphi 7.  Sistem ini telah diuji untuk  melihat ketinggian air laut serta untuk menampilkan grafik pasang surut. Proses pengujian alat berlangsung di Dermaga DIT POL AIR NTT. Hasil pengujian menunjukkan bahwa sistem berjalan dengan baik. Dimana diperoleh data bahwa dalam satu hari pengukuran terjadi dua kali pasang dan dua kali surut yang merupakan tipe pasang surut harian ganda dengan puncak pasang tertinggi adalah 164 cm dan surut terendah dengan ketinggian 68 cm. Kata kunci: pasang surut; sensor ultrasonik HC-SR04; Arduino; Delph

Simulation and Augmentation of Social Networks for Building Deep Learning Models

A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model

Simulation and Augmentation of Social Networks for Building Deep Learning Models

A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model