Vision-based frontal vehicle detection and tracking

This paper presents a vision-based driver assistance system composing of vehicle detection using knowledge-based method and vehicle tracking using Kalman filtering.First, a preceding vehicle is localized by a proposed detection scheme, consisting of shadow detection and brake lights detection.Second, the possible vehicle region is extracted for verification. Symmetry analysis includes contour and brake lights symmetries are performed and followed by an asymmetry contour analysis in order to obtain vehicle’s center.The center of vehicle is tracked continuously using Kalman filtering within a predicted subwindow in consecutive frames.It reduces the scanning process and maximizes the computational speed of vehicle detection. Simulation results demonstrate good performance of the proposed system

Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Multi-level Signal Decomposition for Power Quality Disturbance Classification

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively

Dissection of Synechococcus rubisco large subunit sections involved in holoenzyme formation in Escherichia coli by combinatorial section swapping and sequence analyses

Engineering the CO2-fixing enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) to improve photosynthesis has long been sought. Rubisco large subunits (RbcL) are highly-conserved but because of certain undefined sequence differences, plant Rubisco research cannot fully utilise the robust heterologous Escherichia coli expression system and its GroEL folding machinery. Previously, a series of chimeric cyanobacteria Synechococcus elongatus Rubisco, incorporated with sequences from the green alga Chlamydomonas reinhardtii, were expressed in E. coli; differences in RbcL sections essential for holoenzyme formation were pinpointed. In this study, the remaining sections, presumably not crucial for holoenzyme formation and also the small subunit (RbcS), are substituted to further ascertain the possible destabilising effects of multiple section mutations. To that end, combinations of Synechococcus RbcL Sections 1 (residues 1-47), 2 (residues 48-97), 5 (residues 198-247) and 10 (residues 448-472), and RbcS, were swapped with collinear Chlamydomonas sections and expressed in E. coli. Interestingly, only the chimera with Sections 1 and 2 together produces holoenzyme and an interaction network of complementing amino acid changes is delineated by crystal structure analysis. Furthermore, sequence-based analysis also highlighted possible GroEL binding site differences between the two RbcLs

Induction of Lrp5 HBM-causing mutations in Cathepsin-K expressing cells alters bone metabolism

High-bone-mass (HBM)-causing missense mutations in the low density lipoprotein receptor-related protein-5 (Lrp5) are associated with increased osteoanabolic action and protection from disuse- and ovariectomy-induced osteopenia. These mutations (e.g., A214V and G171V) confer resistance to endogenous secreted Lrp5/6 inhibitors, such as sclerostin (SOST) and Dickkopf homolog-1 (DKK1). Cells in the osteoblast lineage are responsive to canonical Wnt stimulation, but recent work has indicated that osteoclasts exhibit both indirect and direct responsiveness to canonical Wnt. Whether Lrp5-HBM receptors, expressed in osteoclasts, might alter osteoclast differentiation, activity, and consequent net bone balance in the skeleton, is not known. To address this, we bred mice harboring heterozygous Lrp5 HBM-causing conditional knock-in alleles to Ctsk-Cre transgenic mice and studied the phenotype using DXA, μCT, histomorphometry, serum assays, and primary cell culture. Mice with HBM alleles induced in Ctsk-expressing cells (TG) exhibited higher bone mass and architectural properties compared to non-transgenic (NTG) counterparts. In vivo and in vitro measurements of osteoclast activity, population density, and differentiation yielded significant reductions in osteoclast-related parameters in female but not male TG mice. Droplet digital PCR performed on osteocyte enriched cortical bone tubes from TG and NTG mice revealed that ~8–17% of the osteocyte population (depending on sex) underwent recombination of the conditional Lrp5 allele in the presence of Ctsk-Cre. Further, bone formation parameters in the midshaft femur cortex show a small but significant increase in anabolic action on the endocortical but not periosteal surface. These findings suggest that Wnt/Lrp5 signaling in osteoclasts affects osteoclastogenesis and activity in female mice, but also that some of the changes in bone mass in TG mice might be due to Cre expression in the osteocyte population

Clcn7F318L/+ as a new mouse model of Albers-Schönberg disease

Dominant negative mutations in CLCN7, which encodes a homodimeric chloride channel needed for matrix acidification by osteoclasts, cause Albers-Schönberg disease (also known as autosomal dominant osteopetrosis type 2). More than 25 different CLCN7 mutations have been identified in patients affected with Albers-Schönberg disease, but only one mutation (Clcn7G213R) has been introduced in mice to create an animal model of this disease. Here we describe a mouse with a different osteopetrosis-causing mutation (Clcn7F318L). Compared to Clcn7+/+ mice, 12-week-old Clcn7F318L/+ mice have significantly increased trabecular bone volume, consistent with Clcn7F318L acting as a dominant negative mutation. Clcn7F318L/F318L and Clcn7F318L/G213R mice die by 1 month of age and resemble Clcn7 knockout mice, which indicate that p.F318L mutant protein is non-functional and p.F318L and p.G213R mutant proteins do not complement one another. Since it has been reported that treatment with interferon gamma (IFN-G) improves bone properties in Clcn7G213R/+ mice, we treated Clcn7F318L/+ mice with IFN-G and observed a decrease in osteoclast number and mineral apposition rate, but no overall improvement in bone properties. Our results suggest that the benefits of IFN-G therapy in patients with Albers-Schönberg disease may be mutation-specific

Feasibility of Visible Near-Infrared Hyperspectral Imaging in Detection of Calcium Hypochlorite in Sago Flour

The general public perspective on sago flour quality is based on the perceived colour appearances. This contributed to the potential of food fraud by excessive usage of bleaching agents such as calcium hypochlorite (CHC) to alter the product’s colour. Conventional methods to detect and quantify CHC such as titration and chromatography are time-consuming, expensive and limited to laboratory setups only. In this research, visible near-infrared hyperspectral imaging (Vis-NIR HSI) was combined with partial least squares regression (PLSR) model to quantify CHC in pure sago flour accurately and rapidly. Hyperspectral images with the spectral region of 400 nm to 1000 nm were captured for CHC-pure sago mixture samples with CHC concentration ranging from 0.005 w/w% to 2 w/w%. Mean reflectance spectral data was extracted from the hyperspectral images, and was used as inputs to develop the PLSR model to predict the CHC concentration. The PLSR model achieved the commendable predictive results in this study, with Rp = 0.9509, RMSEP = 0.1655 and MAPEP of 3.801%, proving that Vis-NIR HSI can effectively predict the concentration of CHC in sago flour

Growth Study and Biological Hydrogen Production by novel strain Bacillus paramycoides

Industrial revolution has created high dependent on fossil fuels for energy creation. However, combustion of fossil fuels has created excessive amount of greenhouse gases, hence led to climate change. Thus, renewable energy has been proposed to alleviate the environmental pollution issues around the globe. One of the promising renewable energies is green hydrogen energy. Commercialized technologies such as electrolysis and thermochemical reaction are utilized to form hydrogen energy. Nonetheless, these processes require high energy and yet producing greenhouse gases that harm the environment. In this study, biodegradation process to produce hydrogen energy has been explored. To our knowledge, Bacillus paramycoides strain has not yet been investigated for biological hydrogen evolution. Therefore, in this paper, the ability of Bacillus paramycoides to produce biological hydrogen has been studied. The rod-shaped and gram-positive Bacillus paramycoides was identified under scanning electron microscope and gram staining procedure. Furthermore, biological hydrogen generation by Bacillus sp. was experimented for 96 hours. The result shows that 4668 ± 120 ppm cumulative hydrogen gas was generated through dark fermentation process. For Bacillus sp. growth study, lag, log, and stationary phase have been achieved in 96 hours. In a summary, metabolic engineering to degrade abundant biomass wastes is a sustainable pathway to produce hydrogen energy, simultaneously resolve waste management issue around the globe