Method for solving nonlinearity in recognising tropical wood species

Classifying tropical wood species pose a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. Hence, an automatic tropical wood species recognition system was developed at Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia. The system classifies wood species based on texture analysis whereby wood surface images are captured and wood features are extracted from these images which will be used for classification. Previous research on tropical wood species recognition systems considered methods for wood species classification based on linear features. Since wood species are known to exhibit nonlinear features, a Kernel-Genetic Algorithm (Kernel-GA) is proposed in this thesis to perform nonlinear feature selection. This method combines the Kernel Discriminant Analysis (KDA) technique with Genetic Algorithm (GA) to generate nonlinear wood features and also reduce dimension of the wood database. The proposed system achieved classification accuracy of 98.69%, showing marked improvement to the work done previously. Besides, a fuzzy logic-based pre-classifier is also proposed in this thesis to mimic human interpretation on wood pores which have been proven to aid the data acquisition bottleneck and serve as a clustering mechanism for large database simplifying the classification. The fuzzy logic-based pre-classifier managed to reduce the processing time for training and testing by more than 75% and 26% respectively. Finally, the fuzzy pre-classifier is combined with the Kernal-GA algorithm to improve the performance of the tropical wood species recognition system. The experimental results show that the combination of fuzzy preclassifier and nonlinear feature selection improves the performance of the tropical wood species recognition system in terms of memory space, processing time and classification accuracy

An Improved Deep Learning Model for Electricity Price Forecasting

Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically important for effective planning and efficient operation of a power system due to deregulation of electricity industry. However, accurate EPF is very challenging due to complex nonlinearity in the time series-based electricity prices. Hence, this work proposed two-fold contributions which are (1) effective time series preprocessing module to ensure feasible time-series data is fitted in the deep learning model, and (2) an improved long short-term memory (LSTM) model by incorporating linear scaled hyperbolic tangent (LiSHT) layer in the EPF. In this work, the time series pre-processing module adopted linear trend of the correlated features of electricity price series and the time series are tested by using Augmented Dickey Fuller (ADF) test method. In addition, the time series are transformed using boxcox transformation method in order to satisfy the stationarity property. Then, an improved LSTM prediction module is proposed to forecast electricity prices where LiSHT layer is adopted to optimize the parameters of the heterogeneous LSTM. This study is performed using the Australian electricity market price, load and renewable energy supply data. The experimental results obtained show that the proposed EPF framework performed better compared to previous techniques

Online system for automatic tropical wood recognition

There are more than 3000 wood species in tropical rainforests, each with their own unique wood anatomy that can be observed using naked eyes aided with a hand glass magnifier for species identification process. However, the number of certified personnel that have this acquired skills are limited due to lenghty training time. To overcome this problem, Center for Artificial Intelligence & Robotics (CAIRO) has developed an automatic wood recognition system known as KenalKayu that can recognize tropical wood species in less than a second, eliminating laborious manual human inspection which is exposed to human error and biasedness. KenalKayu integrates image acquisition, feature extraction, classifier and machine vision hardware such as camera, interfaces, PC and lighting. Grey level co-occurrence matrix (GLCM) is used for feature extraction. The features are trained in a back-propagation neural network (BPNN) for classification. This paper focusses more on the database development and the online testing of the wood recognition system. The accuracy of the online system is tested on different image quality such as image taken in low light condition, medium light condition or high light condition

Gray Level Co-Occurrence Matrix (GLCM) and Gabor Features Based No-Reference Image Quality Assessment for Wood Images

Image Quality Assessment (IQA) is an imperative element in improving the effectiveness of an automatic wood recognition system. There is a need to develop a No-Reference-IQA (NR-IQA) system as a distortion free wood images are impossible to be acquired in the dusty environment in timber factories. Therefore, a Gray Level Co- Occurrence Matrix (GLCM) and Gabor features-based NR-IQA, GGNR-IQA algorithm is proposed to evaluate the quality of wood images. The proposed GGNR-IQA algorithm is compared with a well-known NR-IQA, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full-Reference-IQA (FR-IQA) algorithms, Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), Feature SIMilarity (FSIM), Information Weighted SSIM (IW-SSIM) and Gradient Magnitude Similarity Deviation (GMSD). Results shows that the GGNR-IQA algorithm outperforms the NR-IQA and FR-IQAs. The GGNR-IQA algorithm is beneficial in wood industry as a distortion free reference image is not required to pre-process wood images

A no-reference image quality assessment metric for wood images

Image Quality Assessment (IQA) is a vital element in improving the efficiency of an automatic recognition system of various wood species. There is a need to develop a No-Reference IQA (NR-IQA) system as a perfect and distortion free wood images may be impossible to be acquired in the dusty environment in timber factories. To the best of our knowledge, there is no NR-IQA developed for wood images specifically. Therefore, a Gray Level Co-Occurrence Matrix (GLCM) and Gabor features-based NR-IQA (GGNR-IQA) metric is proposed to assess the quality of wood images. The proposed metric is developed by training the support vector machine regression with GLCM and Gabor features calculated for wood images together with scores obtained from subjective evaluation. The proposed IQA metric is compared with a widely used NR-IQA metric, Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Full Reference-IQA (FR-IQA) metrics. Results shows that the proposed NR-IQA metric outperforms the BRISQUE and the FR-IQA metrics. Moreover, the proposed NR-IQA metric is beneficial in wood industry as a distortion free reference image is not needed to evaluate the wood image

Vehicles trajectories analysis using piecewise-segment dynamic time warping (PSDTW)

The number of vehicles increases every year in big cities around Malaysia, causing a higher flow of traffic on the road, prompting authorities to increase traffic monitoring to ensure smooth traffic conditions. Traffic surveillance normally conducted using cameras and observed manually by the authorities before they can make decisions on controlling the traffic flow. Continuous monitoring is tedious and prone to error especially during rush hour where traffic volume drastically increased. Intelligent traffic monitoring is possible via trajectory analysis and prediction where the artificial intelligent algorithms learns and cluster the trajectories of vehicles movement. Similarity measure based on distance-based such as Euclidean-distance, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCSS) are less accurate and computationally costly. This paper proposed a combined modified DTW method which merged the piecewise and segmentation measurement in DTW, and the proposed method will be known as the Piecewise-Segment Dynamic Time Warping Distance (PSDTW). PSDTW algorithm is tested on CROSS dataset and compared the time taken and accuracy of the algorithm with previous studied algorithm. The proposed method improves the execution time by average of factor of 4 compared to the SDTW, DTW and LCSS with good results accuracy which have less than 0.01 error rate

Development of Machine Vision System for Riverine Debris Counting

In Malaysia, about 80% of freshwater sources come from rivers, but 44% of rivers are polluted. One of the river cleaning efforts is via Ocean Cleanup's Interceptor river cleaning machine. The efficiency depends on its location at the river, which is highly dependent on debris count along the river currently counted by human manual operators. Unfortunately, the process is not continuous and can only be done few hours in daylight. This project proposed to replace manual counting with a continuous automated debris counting system using computer vision. The system consists of a camera connected to a computer with algorithms that process the river live video feed and automatically detect and count riverine debris. The system was trained using three datasets over two You Only Look Once (YOLOv4) configurations producing six YOLOv4 models. The system was tested on a 5-minutes video of a flowing water source with floating debris, and the system's best performance, to match human counting, was by 110% or 10% better than human counting. This count may assist decision-making in locating the river cleaning interceptor and increase the efficiency of river cleaning activities

YOLO-based network fusion for riverine floating debris monitoring system

Riverine floating debris has been one of the major challenges and a well-known issue across the globe for decades. To mitigate this problem, sources of debris and their pathways to the riverine environment need to be identified and quantified. The scope of this study is to obtain visual information of floating debris which is crucial in developing a robotic platform for riverine management system. Therefore, an object detector using You Only Look Once version 4 (YOLOv4) algorithm is developed to detect floating debris for the riverine monitoring system. The debris detection system is trained on five object classes such as styrofoam, plastic bags, plastic bottle, aluminium can and plastic container. After the first training is conducted, image augmentation technique is implemented to increase training and validation datasets. Finally, the performance of the proposed debris detection system is evaluated based on the highest mean average precision (mAP) weight file, classification accuracy, precision and recall

Systematic review on vehicular licence plate recognition framework in intelligent transport systems

In recent years, vehicular licence plate recognition (VLPR) framework has emerged as one of the most significant issues in intelligent transport systems. It has emerged as an important and complicated issue of research in recent times as explorations are carried on this issue with regard to the challenges and diversities of licence plates (LP) including various illumination and hazardous situations. Restricted situations like stationary background, only one vehicle image, fixed illumination, and limited vehicular speed have been focused in most of the approaches. VLPR approaches should be generalised for being capable of identifying LP containing different fonts, colours, languages, complex backgrounds, deformities, hazardous situations, occlusion, speeding vehicles, vertical or horizontal skew, blurriness, and illumination diversions. A comprehensive investigation on the existing VLPR techniques has been carried throughout this study by the aspects of detecting, segmenting, and recognising the plates. Different existing VLPR approaches have been categorised in accordance with the deployed attributes and the classifications have been compared as well on the basis of conveniences, inconveniences, processing time, and recognition rate when available