Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

Sciuto, Grazia Lo; Capizzi, Giacomo; Shikler, Rafi; Napoli, Christian

doi:10.1002/int.22386

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…Anomalies in connection with criminal activity are the focus of future work wherein a deep learning neural network can be used as a classifier to investigate them. Furthermore, similar applications of machine learning can be explored in different fields [31], such as classifying the defect in organic cells and many more. Similarly, work can be extended to detect the malfunction of solar tracking systems and normal function, referring to [32].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Gait Abnormality Detection Method Based on Classification

Jani

Vijayakumar

Parmar

et al. 2022

JSAN

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In the study of human mobility, gait analysis is a well-recognized assessment methodology. Despite its widespread use, doubts exist about its clinical utility, i.e., its potential to influence the diagnostic-therapeutic practice. Gait analysis evaluates the walking pattern (normal/abnormal) based on the gait cycle. Based on the analysis obtained, various applications can be developed in the medical, security, sports, and fitness domain to improve overall outcomes. Wearable sensors provide a convenient, efficient, and low-cost approach to gather data, while machine learning methods provide high accuracy gait feature extraction for analysis. The problem is to identify gait abnormalities and if present, subsequently identify the locations of impairments that lead to the change in gait pattern of the individual. Proper physiotherapy treatment can be provided once the location/landmark of the impairment is known correctly. In this paper, classification of multiple anatomical regions and their combination on a large scale highly imbalanced dataset is carried out. We focus on identifying 27 different locations of injury and formulate it as a multi-class classification approach. The advantage of this method is the convenience and simplicity as compared to previous methods. In our work, a benchmark is set to identify the gait disorders caused by accidental impairments at multiple anatomical regions using the GaitRec dataset. In our work, machine learning models are trained and tested on the GaitRec dataset, which provides Ground Reaction Force (GRF) data, to analyze an individual’s gait and further classify the gait abnormality (if present) at the specific lower-region portion of the body. The design and implementation of machine learning models are carried out to detect and classify the gait patterns between healthy controls and gait disorders. Finally, the efficacy of the proposed approach is showcased using various qualitative accuracy metrics. The achieved test accuracy is 96% and an F1 score of 95% is obtained in classifying various gait disorders on unseen test samples. The paper concludes by stating how machine learning models can help to detect gait abnormalities along with directions of future work.

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Section: Discussionmentioning

confidence: 99%

An Efficient Gait Abnormality Detection Method Based on Classification

Jani

Vijayakumar

Parmar

et al. 2022

JSAN

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“…They handle every input individually using separate weights. Sciuto et al [4], [22] use elliptical basis neural networks for detecting defects in organic solar cells.…”

Section: Related Workmentioning

confidence: 99%

WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects

Nag,

Makwana,

et al. 2022

Preprint

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As the integration density and design intricacy of semiconductor wafers increase, the magnitude and complexity of defects in them are also on the rise. Since the manual inspection of wafer defects is costly, an automated artificial intelligence (AI) based computer-vision approach is highly desired. The previous works on defect analysis have several limitations, such as low accuracy and the need for separate models for classification and segmentation. For analyzing mixed-type defects, some previous works require separately training one model for each defect type, which is non-scalable.In this paper, we present WaferSegClassNet (WSCN), a novel network based on encoder-decoder architecture. WSCN performs simultaneous classification and segmentation of both single and mixed-type wafer defects. WSCN uses a "shared encoder" for classification, and segmentation, which allows training WSCN end-to-end. We use N-pair contrastive loss to first pretrain the encoder and then use BCE-Dice loss for segmentation, and categorical cross-entropy loss for classification. Use of N-pair contrastive loss helps in better embedding representation in the latent dimension of wafer maps. WSCN has a model size of only 0.51MB and performs only 0.2M FLOPS. Thus, it is much lighter than other state-of-the-art models. Also, it requires only 150 epochs for convergence, compared to 4,000 epochs needed by a previous work. We evaluate our model on the MixedWM38 dataset, which has 38,015 images. WSCN achieves an average classification accuracy of 98.2% and a dice coefficient of 0.9999. We are the first to show segmentation results on the MixedWM38 dataset. The source code can be obtained from https://ckmvigil.github.io/wscn/ Subhrajit Nag is at IIT Hyderabad. Dhruv Makwana and Sai Chandra Teja R are at CKMVigil

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“…However, the device stability was rather poor due to the rapid dissolving of the perovskite materials in organic solvents. Subsequent explorations by many scientists have led to numerous improvements in aspects such as perovskite materials and device structures, thus pushing the research and enthusiasm related to PSCs to a higher level [ 15 , 16 , 17 ]. Among them, one of the most promising route is go all-inorganic, as extensive researches have shown that all-inorganic halide perovskites express superior intrinsic structural stability and much lower sensitivity against humidity environment [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Bulk Photovoltaic Current Mechanisms in All-Inorganic Perovskite Multiferroic Materials

Chen

Gong³

et al. 2023

Nanomaterials

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After the discovery of bulk photovoltaic effect more than half a century ago, ferro-electrical and magneto-optical experiments have provided insights into various related topics, revealing above bandgap open voltages and non-central symmetrical current mechanisms. However, the nature of the photon-generated carriers responses and their microscopic mechanisms remain unclear. Here, all-inorganic perovskite Bi0.85Gd0.15Fe1−xMnxO3 thin films were prepared by a sol-gel process and the effects of Gd and Mn co-doped bismuth ferrites on their microtopography, grain boundries, multiferroic, and optical properties were studied. We discovered a simple “proof of principle” type new method that by one-step measuring the leakage current, one can demonstrate the value of photo generated current being the sum of ballistic current and shift current, which are combined to form the so-called bulk photovoltaic current, and can be related to the prototype intrinsic properties such as magneto-optical coupling and ferroelectric polarization. This result has significant potential influence on design principles for engineering multiferroic optoelectronic devices and future photovoltaic industry development.

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Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

Cited by 19 publications

References 32 publications

An Efficient Gait Abnormality Detection Method Based on Classification

An Efficient Gait Abnormality Detection Method Based on Classification

WaferSegClassNet -- A Light-weight Network for Classification and Segmentation of Semiconductor Wafer Defects

Bulk Photovoltaic Current Mechanisms in All-Inorganic Perovskite Multiferroic Materials

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