Intelligent detection of the falls in the elderly using fuzzy inference system and video-based motion estimation method

2013 ◽  
Author(s):  
Khosro Rezaee ◽  
Javad Haddadnia ◽  
Ahmad Delbari
Keyword(s):  
Motion Estimation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Estimation Method ◽  
The Elderly ◽  
Inference System ◽  
Intelligent Detection

scholarly journals Evaluation of electrical load estimation in Diyala governorate (Baaquba city) based on fuzzy inference system

2021 ◽  
Vol 11 (5) ◽  
pp. 3757
Author(s):  
Siraj Manhal Hameed ◽  
Hayder Khaleel AL-Qaysi ◽  
Ali Sachit Kaittan ◽  
Mohammed Hasan Ali
Keyword(s):  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Estimation Method ◽  
Load Estimation ◽  
Electrical Load ◽  
Electrical Power System ◽  
Inference System ◽  
Electrical Generation

The evaluation of electrical load estimation is requisitely of any electrical power system. This manner is needed for system obligation, economical distribution and maintenance time of electrical system. In this paper, we propose electrical load estimation method based on fuzzy inference system which gives accurate results for estimated loads in Iraq (Diyala governorateBaaquba city). And it can assist the electrical generation and distribution system that depends on important parameters (temperature, humidity and the speed of the wind). By considering the parameters temperature, humidity and the speed of the wind. These parameters are applied as inputs to the fuzzy logic control system to obtain the normalize estimated load as output by electing membership functions. It is exceptionally valuable to form a choice by taking into consideration these assessed readings that come to from the proposed FIS that displayed in this paper with precision of 0.969 from the real stack request.

Personalized adaptive system for elderly care in smart home using fuzzy inference system

2018 ◽  
Vol 14 (3/4) ◽  
pp. 210-232 ◽  
Author(s):  
Kurnianingsih Kurnianingsih ◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan ◽  
Lutfan Lazuardi ◽  
Anton Satria Prabuwono ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Smart Home ◽  
Fuzzy Inference ◽  
Adaptive System ◽  
Elderly Care ◽  
The Elderly ◽  
Positioning System ◽  
Alert System ◽  
Inference System ◽  
Content Type

Purpose The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies. Design/methodology/approach The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy. Findings The results of the experiment showed that FSC produced the best F1-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system. Social implications Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness. Originality/value This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

Sign in / Sign up

Export Citation Format

Share Document