scholarly journals Pengaturan Fan Speed dan Suhu Air Conditioner Melalui Ucapan Dengan Layanan Google Assistant API

2020 ◽  
Vol 21 (2) ◽  
pp. 170
Author(s):  
Michael Albertus ◽  
Muliady Muliady
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Sound Intensity ◽  
Infrared Light ◽  
Raspberry Pi ◽  
Air Conditioner ◽  
Verbal Abilities ◽  
Verbal Control ◽  
Fan Speed

Air conditioner control using speech recognition is made to help  people with disabilities that unable to operate remote control physically but have verbal abilities. Verbal control allows disabled people adjust temperatures and fan speed. Speech received by Respeaker 2-mics Pi HAT module, converted into wav, processed by Google Assistant API with Natural Language Processing algorithm by categorizing words into their types, subject, predicate, object, and description to facilitate Google Assistant API. Words matched with command in command text database Raspberry Pi 3 to enable local commands modulates signal in form of space-coded signal on GPIO, transmitted through infrared transmitter to control the Air Conditioner. Infrared database obtained by receiving infrared signal through infrared receiver that have been coded by LIRC into pulse space, calling function is created, compared in command text database.The infrared light distance from the infrared transmitter can be sent to air conditioner up to 600 cm with β NPN 2N2222A transistor worth 257, Resistor base value is 1500 Ohm, and Resistor collector value is 6.2 Ohm. Speech to text experiments with background sound intensity 35-40 dB, respondent’s sound intensity 50-70dB, and the respondent’s distance to the microphone 40-50 cm. System can recognize respondent’s speeches with success rate above 50%. The word “High” in fan speed speech experiments cannot be detected by the system, so it is necessary to add other word to be recognized. The system can receive Google Translate speech and only got one failure.ABSTRAK:Perangkat elektronik air conditioner dengan pengendalian pengenalan ucapan untuk membantu penyandang tunadaksa yang tidak mampu mengendalikan remote secara fisik tetapi memiliki kemampuan verbal. Pengendalian dengan cara verbal memungkinkan penyandang tunadaksa untuk mengubah suhu dan mengatur fan speed air conditioner. Ucapan diterima modul Respeaker 2-mics Pi HAT dikonversi menjadi format wav kemudian diolah oleh Google Assistant API dengan algoritma Natural Language Processing yaitu mengategorikan kata menjadi jenisnya, subjek, predikat, objek, dan keterangannya untuk mempermudah pencarian pada kamus Google Assistant API. Kata tersebut dibandingkan dengan perintah ucapan pada commandtextdatabaseRaspberry Pi 3 yang mengaktifkan local command dan memodulasi sinyal space-coded signal pada GPIO, ditransmisikan melalui infrared transmitter untuk mengatur air conditioner. Infrareddatabase diperoleh melalui penerimaan cahaya infrared dan dikodekan menjadi pulse space oleh software LIRC menjadi fungsi pemanggilan, dipasangkan dengan perintah ucapan commandtextdatabase. Jarak cahaya infrared dari infrared transmitter dapat dikirimkan ke air conditioner hingga sejauh 600 cm dengan  transistor NPN 2N2222A bernilai 257, nilai Resistor base sebesar 1500 Ohm, dan nilai Resistor collector sebesar 6,2 Ohm. Uji cobaspeech to text dengan kondisi intensitas background sound 35-40 dB, intensitas suara responden 50-70 dB, dan jarak responden ke microphone 40-50 cm. Sistem yang direalisasi mampu mengenali ucapan yang diberikan responden dengan keberhasilan di atas 50%. Ucapan “High” pada pengujian ucapan fan speed tidak dapat dideteksi oleh sistem, oleh karena itu perlu ditambahkan ucapan suhu agar ucapan dikenal. Sistem mampu menerima ucapan Google Translate dan hanya mendapatkan satu kali kegagalan deteksi ucapan

scholarly journals Home Automation Using Chatbot and Voice Assistant

2020 ◽  
Vol 32 ◽  
pp. 01002
Author(s):  
Bhavyasri Kadali ◽  
Neha Prasad ◽  
Pranaya Kudav ◽  
Manoj Deshpande
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Raspberry Pi ◽  
Automation System ◽  
Home Automation ◽  
Voice Input ◽  
Text Input ◽  
Automation Systems ◽  
Home Automation System

In a world with ever increasing needs for comfort, human race is relying more and more on technological advancements to find solutions to their problems. Home Automation Systems have become a go-to arena in the recent years. In the following paper, we propose a Home Automation system that uses a wholesome blending of some technologies like Internet of Things, Natural Language Processing and Machine Learning. The prime feature of this system is that, it provides two modes of communication to the user : Text and Voice. The text input from the user will be given via a Chatbot Application and the voice input from the user will be given via a voice assistant. The input will undergo Natural Language Processing to find the action that the user wants the system to perform. The IoT component, Raspberry Pi would perform the actuations in the form of switching On or Off of Lights and Fans of a room in the house.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

PRINCIPAL PROBLEMS OF NATURAL LANGUAGE PROCESSING SYSTEMS

2018 ◽  
pp. 35-38
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Analysis ◽  
Syntactic Ambiguity ◽  
Grammatical Structure ◽  
English Sentence ◽  
Analysis Methods ◽  
The Way

The article deals with natural language processing, namely that of an English sentence. The article describes the problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to analyze the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them.

Sign in / Sign up

Export Citation Format

Share Document