Improving sensor data analysis through diverse data source integration

2009 ◽  
Author(s):  
Jennifer Casper ◽  
Ronald Albuquerque ◽  
Jeremy Hyland ◽  
Peter Leveille ◽  
Jing Hu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Sensor Data ◽  
Data Source Integration ◽  
Data Source ◽  
Diverse Data

Improving data analysis through diverse data source integration

2009 ◽  
Author(s):  
Ronald Albuquerque ◽  
Jennifer Casper ◽  
Ed Cheung ◽  
Ron Couture ◽  
Barry Lai ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Source Integration ◽  
Data Source ◽  
Diverse Data

KORESPONDENSI FONEMIS BAHASA SUNDA DAN BAHASA JAWA

IdeBahasa ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 121-132
Author(s):  
Shifa Nur Zakiyah ◽  
Susi Machdalena ◽  
Tb. Ace Fachrullah
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Interview Techniques ◽  
Data Source ◽  
Historical Comparative ◽  
Linguistic Approach ◽  
The Relationship ◽  
Recording Techniques

This article discussed the phonemic correspondence in Sundanese and Javanese using a historical comparative linguistic approach. The problem to be examined in this study is the form of phonemic correspondence in Sundanese and Javanese. The purpose of this study was to determine the phonemic correspondence sets in the comparison between Sundanese and Javanese. The method used in this research to analyze the data is the phonemic correspondence method. The correspondence method is used to find the relationship between languages ​​in the field of language sounds (phonology). Phonemic correspondence is used to determine regular phonemic changes in the languages ​​being compared. Data collection used interview techniques, note techniques and recording techniques. After the data is collected, then the data is classified according to the problem being studied and grouped into more specifics. After that, conclusions will be made based on the results of the data analysis. The data source obtained comes from 200 swadesh vocabularies in Sundanese and Javanese. From 200 swadesh vocabulary data found 49 data included in phonemic correspondence which is divided into 12 correspondence sets. The results of this study include the formation of correspondences in Sundanese and Javanese, namely, (ɛ ~ i) and (i ~ ɛ), (a ~ ɔ) and (ɔ ~ a), (d ~ D), (t ~ T) , (ɤ ~ ə), (b ~ w), (ɔ ~ u) and (ɔ ~ U), (i ~ I), (ø ~ h) and (h ~ ø), (ø ~ m), and (a ~ ə).

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

scholarly journals Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2884 ◽  
Author(s):  
Xiaobo Chen ◽  
Cheng Chen ◽  
Yingfeng Cai ◽  
Hai Wang ◽  
Qiaolin Ye
Keyword(s):  
Data Analysis ◽  
Sparse Representation ◽  
Intelligent Transportation Systems ◽  
Missing Values ◽  
Road Traffic ◽  
Linear Representation ◽  
Transportation Systems ◽  
Sensor Data ◽  
L1 Norm ◽  
L2 Norm

The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.

scholarly journals ANALISIS KESALAHAN BERBAHASA DALAM PIDATO MAHASISWA MPBI-UMS [Analysis of Language Errors in MPBI-UMS Student Speech]

TOTOBUANG ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 185-196
Author(s):  
Wara Angreni ◽  
Atiqa Sabardila
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Research Method ◽  
Distribution Method ◽  
Student Speech ◽  
Reading Technology ◽  
Data Source ◽  
Phonological Change ◽  
Phonological Errors ◽  
Matching Techniques

This study aims to describe the form of speech errors of the candidates for Regional Head of Kulon Progo Regency. The research method used is qualitative descriptions. The data source is the utterances of the student speech. The data collection techniques are listening and note-taking. The data analysis used referential matching techniques and articulatory phonetic equivalents, extension techniques in the distribution method and sign reading technology. The results of the study shows that there are language errors in the form of speech of the candidates for Regional Head of Kulon Progo Regency The five areas of error are (1) phonological errors including phonological change, phoneme formation and pronunciation, (2) morphological errors including prepositions, repetition, tone, and combination of meN- and -kan prefixes, (3) syntax errors including ambiguous sentences, redundant words, and unclear sentence types (4) sociolinguistic errors, including misuse of language coding in sentences, and (5) spelling errors in capital letters, and punctuation.  Penelitian ini bertujuan mendeskripsikan bentuk kesalahan berbahasa pidato mahasiswa calon kepala daerah Kabupaten Kulon Progo. Metode penelitian menggunakan deskripsi kualitatif. Data penelitian berupa tuturan pidato mahasiswa. Teknik pengumpulan simak dan catat.  Analisis data menggunakan teknik padan referensial dan padan fonetis artikulatoris, teknik perluasan dalam metode agih dan teknologi membaca tanda. Hasil penelitian terjadi kesalahan bahasa pada bentuk tuturan pidato mahasiswa calon kepala daerah Kabupaten Kulon Progo memiliki lima wilayah kesalahan yaitu (1) kesalahan fonologi termasuk kesalahan perubahan fonem, kesalahan pembentukan dan pengucapan fonem, (2) kesalahan morfologi meliputi preposisi, penulisan ulang, bentuk nada, dan tulis kombinasi prefiks meN- dan -kan, (3) kesalahan sintaks termasuk kalimat yang ambigu, rancu, kata-kata yang berlebihan, jenis kalimat yang tidak jelas (4) kesalahan sosiolinguistik, termasuk penyalahgunaan campur kode bahasa dalam kalimat, dan (5) kesalahan ejaan dalam huruf kapital, dan tanda baca.

scholarly journals Identifikasi Hidangan pada Upacara Kesempatan Khusus Suku Tengger di Desa Argosari Kecamatan Senduro Kabupaten Lumajang

2021 ◽  
Vol 1 (3) ◽  
pp. 232-243
Author(s):  
Tria Maryani ◽  
Soenar Soekopitojo ◽  
Titi Kiranawati
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Data Presentation ◽  
Descriptive Research ◽  
Qualitative Descriptive ◽  
Special Occasion ◽  
Data Source ◽  
Staple Foods ◽  
Side Dishes

Tengger tribe has a dish that is served on special occasions ceremony. The special occasion ceremony held by the Tengger tribe is inseparable from the culture and Hindus’s religion which is the majority religion of the Tengger tribe. Argosari Village is one of the villages that still carries out all special occasion’s ceremonies related to custom and religion. This village is in Lumajang Regency. This research was using qualitative descriptive research, with data collection techniques used are interviews, observation, and documentation. The data source was obtained from interviews with five informants are the Dukun Pandhita of Argosari village, Mangku village, and society’s Argosari. Data analysis is performed interactively with steps such as reduction, data presentation and drawing conclusions or verification and to check the validity of finding with member checking methode to Dukun Pandhita. The results of the research which is identifiying the special occasion dishes of Tengger tribe, there are 6 groupings dishes based on staple foods such as rice, side dishes such as omelette, fried chicken, fried noodles. Vegetable dishes include jangan benguk, jangan kentang. Snacks consist of pepes, pasung, jenang abang, apem, juadah, tetelan. The beverages such as tea and coffee. Dandananan such as gedhang ayu, pencok bakal. Masyarakat suku Tengger mempunyai hidangan yang disajikan pada upacara kesempatan khusus. Upacara kesempatan khusus yang dilaksanakan oleh masyarakat suku Tengger tidak terlepas dari kebudayaan dan agama Hindu yang merupakan agama mayoritas suku Tengger. Desa Argosari merupakan salah satu desa yang masih melaksanakan semua upacara kesempatan khusus yang berkaitan dengan adat maupun keagamaan, Desa ini terletak di Kabupaten Lumajang. Penelitian ini merupakan penelitian deskriptif kualitatif, dengan teknik pengumpulan data berupa wawancara, observasi dan dokumentasi. Sumber data diperoleh dari wawancara dengan lima informan yaitu Dukun Pandhita teraktif dengan langkah reduksi, penyajian data dan penarikan kesimpulan atau verifikasi. Pengecekan temuan dilaksanakan dengan member checking kepada Dukun Pandhita. Hasil dari penelitian ini adalah terdapat 6 pengelompokan hidangan yang disajikan pada kesempatan khusus, yaitu makanan pokok antara lain nasi, hidangan lauk pauk berupa telur dadar, ayam goreng. Hidangan sayuran antara lain jangan benguk, jangan kentang. Sedap-sedapan yang terbagi menjadi jajanan telesan berupa pepes, pasung, jenang abang, apem, juadah, tetelan dan jajanan garingan seperti matari

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