İnverter klima akıllı etkileşim sistem tasarımı

Yıl 2018, Cilt: 24 Sayı: 2, 205 - 213, 30.04.2018

Murat Taşkıran Nihan Kahraman , Halime Usta Yoğun

Öz

Bilgisayarların
gündelik hayatın bir parçası olmasıyla birlikte insan-bilgisayar etkileşimi
önem kazanmaya başlamıştır. Geleneksel insan bilgisayar arayüzleri olan klavye,
fare gibi aygıtlar yaygın olarak kullanılmasına rağmen kullanıcı ile bilgisayar
arasındaki bilgi ve komut akışını sınırlamaktadır. Son dönemlerde jestler
sadece çevre ve insanlar arası iletişimde değil, insan-makina iletişimde de
önemli bir role sahip olmaya başlamıştır. Jestlerin ne anlama geldiği ve nasıl
bir bilgi taşıdığı alanındaki çalışmalar gün geçtikçe insan-makina arayüzü
(iletişimi) çalışmalarında daha fazla yer almaya başlamıştır. Bu çalışmada amaç
elektronik cihazlardan biri olan inverter klimaların kontrolünü kullanıcı ile
makina arasında direkt bir etkileşim sağlayarak, kullanıcının daha kolay ve
aracıdan bağımsız olarak inverter klima ile haberleşmesini sağlamaktır.
Önerilen sistemde el jestinin tanınması amacı ile öncelikle kameradan
görüntüler alınmış ve bu görüntülere literatürde bulunan görüntü ön-işleme
teknikleri ve filtreleri kullanılarak, görüntünün netleştirilmesi ve
gürültülerin temizlenmesi sağlanmıştır. Ön-işleme işlemi sonucunda elde edilen
görüntülerde ten rengi tespiti ve Yönlü Gradyanlar Histogramı (YGH)
algoritmaları kullanılarak el jestinin konumu belirlenmiştir. Daha sonra
görüntü işleme algoritmalarından elde edilen konum bilgileri yapay sinir ağına
giriş olarak verilerek el jestinin hareketi anlamlı hale getirilmiş ve inverter
klimada %92 başarı ile sıcaklık ve fan ayarları kontrolü sağlayan bir algoritma
geliştirilmiştir.

Anahtar Kelimeler

İnverter klima, Akıllı sistem tasarımı, Morfolojik işlemler, Yönlü gradyanlar histogramı, Çok katmanlı algılayıcılar, k–en yakın komşuluk

Inverter air conditioner intelligent system design

Öz

As the
computers are becoming a part of people’s daily lives, human-computer
interaction has started to gain more importance. Even though the traditional
human computer interfaces like the keyboards and mouses are used frequently,
they are making the information and command flow limited. Recently, gestures
are not only gaining a more important role in the environment-human relations
but also in human-machine interactions as well. The studies on the meaning of
the gestures and what kind of information they carry are increasing their
places in researches on human-machine interface communication. The aim in this
study is to provide an easy and user independent human -machine interface
system in air conditioners. In the proposed system, in order to recognize hand
gestures, first image preprocessing techniques and noise filters have been
applied to images taken from camera. These processes provide clarification of
image. After preprocessing, Histogram of Oriented Gradients (HOG) algorithm is
used along with the skin color detection made via image processing techniques
applied in order to the recognition of hand gestures. After then, the hand
gestures have made meaningful as the obtained images are given as inputs to
artificial neuron network and an algorithm, which ensures fan and temperature
settings with 92% success level in inverter air conditioners, is developed.

Anahtar Kelimeler

Inverter air conditioner, Intelligent system design, Morphologic process, Histogram of oriented gradients, Multi-Layer perceptron, k-nearest neighborhood

Kaynakça

• Preetham S, Sanath S, Sreevatsa GS, Sumanth M, Varun PM, Kumar DNK. “Smart console for vehicles”. Texas Instruments India Educators' Conference (TIIEC), Bangalore, India, 4-6 April 2013.
• Ju J, Han D, Kim J, Lee I, Cha J, Kim J. “A new control owner switching system for multiple TV viewers via face recognition”. IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 9-12 January 2015.
• Thavalengal S, Bigioi P, Corcoran, P. “Evaluation of combined visible/NIR camera for iris authentication on smartphones”. IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 7-12 June 2015.
• Chaudhar A, Raheja JL, Das K, Raheja S. “Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey”. International Journal of Computer Science & Engineering Survey (IJCSES), 2(1), 122-133, 2011.
• Ohn-Bar E, Trivedi MM. “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations”. IEEE Transactions on Intelligent Transportation Systems, 15(6), 2368-2377, 2014.
• Renuka H, Goutam, B. “Hand gesture recognition system to control soft front panels”. International Journal of Engineering Research & Technology (IJERT), 3(12), 5-10, 2014.
• Lee D, Park Y. “Vision-based remote control sytem by motion detection and open finger counting”. IEEE Transactions on Consumer Electronics, 55(4), 2308-2313, 2009.
• Zhou R, Yuan J, Zhang Z. "Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera". Proceedings of the 19th ACM international conference on Multimedia MM, Scottsdale, Arizona, USA, 28 November-1 December 2011.
• Nasrollahi K, Moeslund TB, Rashidi M. “Haar-like rectangular features for biometric recognition”. International Conference on Biometrics, Madrid, Spain, 4-7 June 2013.
• Gurav MR, Kadbe PK. “Real time finger tracking and contour detection for gesture recognition using OpenCV”. International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 28-30 May 2015.
• Swetha A, Sheeba MS. “Moving object tracking ın video scenes on embedded linux and beagleboard-Xm”. International Journal of VLSI and Embedded Systems-I, 5, 764-768, 2014.
• Vignesh S, Saravanan P. “Real time hand gesture recognition for human machine communication using ARM cortex A-8”. IOSR Journal of Computer Engineering (IOSR-JCE), 16(2), 43-48, 2014.
• Acay E, Kahraman N, Taşkıran M, Kıyan T, Usta Yoğun H. “Hardware application of human-machine ınterface in smart air conditioners using hand tracking”. 58th International Symposium ELMAR, Zadar, Croatia, 12-14 September 2016.
• Panasonic, Aircon “Panasonic Aircon Isıtma ve Soğutma Çözümleri”. http://www.aircon.panasonic.eu/CZ_cs/happening/585/ (06.07.2016).
• Karakaya F, Altun H, Çavuşlu. MA. “Gerçek zamanlı nesne tanıma uygulamaları için HOG algoritmasının FPGA tabanlı gömülü sistem uyarlaması”. IEEE 17th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 9-11 April 2009.
• Tevetoğlu H O, Kahraman N. “Design of a human-machine interface control system for home air conditions”. Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16-19 May 2015.
• Özcan T. Hareketli Nesnelerde Yüz Tespitine Yönelik Bir Uygulama. Yüksek Lisans Tezi, Trakya Üniversitesi Fen Bilimleri Enstitüsü, Edirne, Türkiye, 2010.
• Maleki M, Eroglu K, Aydemir O, Manshoori N, Kayikcioglu T. “A new method for selection optimum k value in k-NN classification algorithm”. 21st Signal Processing and Communications Applications Conference (SIU), Haspolat, Turkey, 24-26 April 2013.
• Dalal N, Triggs B, “Histograms of oriented gradients for human detection”. International Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20-25 June 2005.
• Bay H, Tuytelaars T, Gool LV. “Surf: Speeded up robust features”. Computer Vision and Image Understanding, 110(3), 346-359, 2006.
• Zhu Q, Mei-Chen Y, Kwang-Ting C, Avidan S, “Fast human detection using a cascade of histograms of oriented gradients”. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA, 17-22 June 2006.
• Güneş F, Demirel F, Mahouti P. “Design of a front–end amplifier for the maximum power delivery and required noise by HBMO with support vector microstrip model”. Radioengineering, 23(1), 134-143, 2014.
• Güneş F, Demirel S, Mahouti P. “A simple and efficient honey bee mating optimization approach to performance characterization of a microwave transistor for the maximum power delivery and required noise”. International Journal of Numerical Modeling Electronics Network, Devices and Fields, 29(1), 4-20, 2015.
• Hoang DC, Yadav P, Kumar R, Panda SK. "Real-Time ımplementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks". IEEE Transactions on Industrial Informatics, 10(1), 774-783, 2014.
• Anochi JA, Velho C, HF. “Optimization of feedforward neural network by multiple particle collision algorithm”. 2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI), Orlando, FL, USA, 9-12 Dec 2014.
• Turky AM, Abdullah S, Sabar NR. “Electromagnetic algorithm for tuning the structure and parameters of neural networks”. IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6-11 July 2014.
• Ojha VK, Abraham A, Snášel V. “Simultaneous optimization of neural network weights and active nodes using metaheuristics”. 14th International Conference on Hybrid Intelligent Systems (HIS), Kuwait, 14-16 December 2014.
• Zhu J, Li X. “An effective meta-heuristic for no-wait job shops to minimize makespan”. IEEE Transactions on Automation Science and Engineering, 9(1), 189-198, 2012.
• Taşkıran M, Çam ZG, Kahraman N. “An efficient metho to optimize multi-layer perceptron for classification of human activities”. 2nd International Conference on Computer, Control and Communication Technologies (CCCT'15), Antalya, Turkey, 3-4 December 2015.
• Pamuk, N.“Enerji sistemlerinde yapay arı kolonisi (yak) algoritması kullanarak yük akışı optimizasyonu”. 2013 Akdeniz Üniversitesi Akademik Bilişim Konferansı, Antalya, Türkiye, 23-25 Ocak 2013.
• Zhang Q, Chen F, Liu X. “Hand gesture detection and segmentation based on difference background ımage with complex background”. The 2008 International Conference on Embedded Software and Systems (ICESS2008), Sichuan, China, 29-31 July 2008.
• Bousaaid M, Ayaou T, Afdel T, Estraillier P. “Hand gesture detection and recognition in cyber presence interactive system for E-Iearning”. International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco, 14-16 April 2014.
• Huang Y, Chen Y, Cheng F. “Hand gesture detection and extraction”. IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Beijing, China, 6-10 July 2013.
• Yushan Y, Sheng B, Yaoyang M, Weiheng Q. “Real-Time gesture recognition system based on camshift algorithm and haar-like feature”. 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Chengdu, China, 19-22 June 2016.
• Hsieh C, Liou D. “A real time hand gesture recognition system using motion history ımage”. 2nd International Conference on Signal Processing Systems (ICSPS), Dalian, China, 5-7 July 2010.
• Chen Q, Georganas ND, Petriu EM. “Real-time Vision-based hand gesture recognition using haar-like features”. Instrumentation and Measurement Technology Conference- IMTC 2007, Warsaw, Poland, 1-3 May 2007.
• Prasuhn L, Oyamada Y, Mochizuki Y, Ishikawa H. “A Hog-Based hand gesture recognition system on a mobile device”. IEEE International Conference on Image Processing (ICIP), Paris, France, 27-30 October 2014.
• Zhao Y, Wang W, Wang Y. “A real-time hand gesture recognition method”. International Conference on Electronics, Communications and Control (ICECC), Ningbo, China, 9-11 September 2011.