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Full-Text Articles in Engineering
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Faculty of Engineering and Information Sciences - Papers: Part B
With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized …
A Novel Monte Carlo-Based Neural Network Model For Electricity Load Forecasting, Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
A Novel Monte Carlo-Based Neural Network Model For Electricity Load Forecasting, Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the …
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Faculty of Engineering and Information Sciences - Papers: Part B
Knowledge tracing is a key area of research contributing to personalized education. In recent times, deep knowledge tracing has achieved great success. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-based Knowledge Tracing with Heterogeneous Information Network Embedding (AKTHE). First, we describe questions and their attributes with a heterogeneous information network and generate meaningful node embeddings. Second, we capture the relevance of historical data …
Ensemble Neural Network Method For Wind Speed Forecasting, Binbin Yong, Fei Qiao, Chen Wang, Jun Shen, Yongqiang Wei, Qingguo Zhou
Ensemble Neural Network Method For Wind Speed Forecasting, Binbin Yong, Fei Qiao, Chen Wang, Jun Shen, Yongqiang Wei, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Wind power generation has gradually developed into an important approach of energy supply. Meanwhile, due to the difficulty of electricity storage, wind power is greatly affected by the real-time wind speed in wind fields. Generally, wind speed has the characteristics of nonlinear, irregular, and non-stationary, which make accurate wind speed forecasting a difficult problem. Recent studies have shown that ensemble forecasting approaches combining different sub-models is an efficient way to solve the problem. Therefore, in this article, two single models are ensembled for wind speed forecasting. Meanwhile, four data pre-processing hybrid models are combined with the reliability weights. The proposed …
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Parsimonious Network Based On A Fuzzy Inference System (Panfis) For Time Series Feature Prediction Of Low Speed Slew Bearing Prognosis, Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam Glowacz
Faculty of Engineering and Information Sciences - Papers: Part B
In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic …
Modeling And Identification Of A Realistic Spiking Neural Network And Musculoskeletal Model Of The Human Arm, And An Application To The Stretch Reflex, Manish Sreenivasa, Ko Ayusawa, Yoshihiko Nakamura
Modeling And Identification Of A Realistic Spiking Neural Network And Musculoskeletal Model Of The Human Arm, And An Application To The Stretch Reflex, Manish Sreenivasa, Ko Ayusawa, Yoshihiko Nakamura
Faculty of Engineering and Information Sciences - Papers: Part B
This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. …