市販センサーとスパイキングニューラルネットワークとの組み合わせによる2成分系の混合揮発性有機溶剤蒸気の分離計測シミュレーション

本研究の目的は，揮発性有機化合物蒸気（Volatile organic compounds： VOC）の混合物の分離計測を可能とするセンサーシステムを開発する事である． VOCは主に有機溶剤から揮発し蒸気となって空気中に存在している． 有機溶剤は優れた特質を持つ一方で，取扱者が揮発した有機化合物を呼吸により取り込み，健康を害することがある．これを防ぐために取扱者のばく露量を管理する必要がある．有機溶剤の呼吸に伴うばく露量を管理するためには，取扱い作業を行なっている場所付近のVOC濃度をリアルタイムで計測するのが効果的である．そのためのVOCセンサーが市販されている．しかし，環境中に複数種類のVOCが存在する場合，それらの総和が出力される．分離計測できるVOCセンサーは実用化されていない．有機溶剤は物質ごとに毒性が大きく異なるので，VOCの総和のみの計測では，その環境のリスクは判断できない．本論文では，まず上記の職域における問題が実際にはどのような状況であるかを確認するため，小規模印刷事業場において個人ばく露測定やVOCメーターによる計測を併用し現地確認を行った．その結果，職域におけるVOCメーターの有効性や問題点が明らかになった．提案するセンサーシステムは，3層のスパイキングニューラルネットワークであり，混合VOCに対する市販の2種類の特性の異なる半導体のセンサーの出力から濃度応答曲面を教師なし学習するものである．学習した濃度応答曲線から混合VOCのそれぞれのガス濃度を算出する．本スパイキングニューラルネットワークでは隠れ層のニューロン群が適度に同期して発火することが必要である．この適度に同期した発火は確率共鳴とノイズ誘導同期を同時に発生させるニューロンによってなされる．市販のガスセンサーの濃度応答特性を元に，スパイキングニューラルネットワークの計算機シミュレーションを行い，2種のVOCが任意に混合されたガスに対し，それぞれの濃度を推定するシミュレーションを行った．学習後のセンサーシステムは任意の濃度で混合されたエタノールとイソブタンを十分な精度で分離計測することができた．九州工業大学博士学位論文 学位記番号：生工博甲第375号 学位授与年月日：令和2年3月25日第1章．序論|第2章．印刷事業場における実地調査|第3章．ニューロンの確率共鳴とノイズ同期|第4章．スパイキングニューラルネットワークを用いた2種の混合気体の分離定量|第5章．結論|第6章．参考文献九州工業大学令和元年

Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks

Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks

Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains