• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1273-1293
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• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Membrane and Water Treatment
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• v.3 no.2
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• pp.113-121
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• 2012

Up-flow multi-layer bioreactor (UMBR) is a hybrid system using dual sludge that consists of an up-flow multi-layer bioreactor as anaerobic/anoxic suspended growth microorganisms followed by an aeration tank. The UMBR acts as a primary settling tank, anaerobic/anoxic reactor, thickener which requires low energy due to mixing by up-flow stream. This study focused on using a pilot UMBR plant with capacity of 20-30 $m^3$/day for domestic wastewater in HCMC. HRTs of UMBR and aeration tank were 4.8 h and 7.2 h, respectively. The average MLSS of UMBR ranged from 10,000-13,600 mg/l SS. Internal recycle rate and sludge return were 200-300% and 150-200%, respectively. The results obtained from this study at flow rate of 20 $m^3$/day showed that removal of COD, SS, TKN, N-$NH_4$, T-N, and color were 91%, 87%, 86%, 80%, 91% and 91%, respectively.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Geosystem Engineering
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• v.21 no.5
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• pp.251-261
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• 2018

With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.

• Structural Engineering and Mechanics
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• v.82 no.3
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Journal of Animal Science and Technology
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• v.64 no.1
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• pp.183-186
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• 2022

Lactiplantibacillus plantarum (L. plantarum) ST was isolated from De'ang pickled tea in Yunnan Province, China. The genomes of strain ST were fully sequenced and analyzed using the PacBio RS II sequencing system. Our previous study has shown that L. plantarum ST is a potential probiotic strain. It had strong tolerance in the simulated artificial gastrointestinal tract, and in the antagonism tests, this strain showed strong antibacterial activity. Therefore, as a probiotic, it may be used in animal breeding. L. plantarum ST genome was composed of 1 circular chromosome and 7 plasmids. The length of the whole genome was 3320817 bp, and the annular chromosome size was 3058984 bp, guanine + cytosine (G ± C) content (%) was 44.76%, which contained 2945 protein-coding sequences (CDS). This study will contribute to a further comprehensive understanding of L. Plantarum ST at the genomic level and provide a theoretical basis for its future application in animal breeding.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.293-299
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• 2021

Purpose: To accelerate magnetic resonance fingerprinting (MRF) by developing a flexible deep learning reconstruction method. Materials and Methods: Synthetic data were used to train a deep learning model. The trained model was then applied to MRF for different organs and diseases. Iterative reconstruction was performed outside the deep learning model, allowing a changeable encoding matrix, i.e., with flexibility of choice for image resolution, radiofrequency coil, k-space trajectory, and undersampling mask. In vivo experiments were performed on normal brain and prostate cancer volunteers to demonstrate the model performance and generalizability. Results: In 400-dynamics brain MRF, direct nonuniform Fourier transform caused a slight increase of random fluctuations on the T2 map. These fluctuations were reduced with the proposed method. In prostate MRF, the proposed method suppressed fluctuations on both T1 and T2 maps. Conclusion: The deep learning and iterative MRF reconstruction method described in this study was flexible with different acquisition settings such as radiofrequency coils. It is generalizable for different in vivo applications.

• Journal of Acupuncture Research
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• v.40 no.1
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• pp.44-52
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• 2023

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion: The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.

• Biomaterials and Biomechanics in Bioengineering
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• v.1 no.2
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• pp.81-93
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• 2014

An in vitro cell study evaluating cell adhesion to hydroxyapatite (HA) coated prosthetic Ti-6Al-4V alloy via laser treatment is presented in comparison with uncoated alloy. Based on our previous in vitro biocompatibility study, which demonstrated higher cell attachment and proliferation with MC3T3-E1 preosteoblast cells, the present investigation aims to reveal the effect of laser coating Ti alloy with HA on the adhesion strength of bone-forming cells against centrifugal forces. Remaining cells on different substrates after centrifugation were visualized using fluorescent staining. Semi-quantifications on the numbers of cells were conducted based on fluorescent images, which demonstrated higher numbers of cells retained on HA laser treated substrates post centrifugation. The results indicate potential increase in the normalized maximum force required to displace cells from HA coated surfaces versus uncoated control surface. The possible mechanisms that govern the enhancing effect were discussed, including surface roughness, chemistry, wettability, and protein adsorption. The improvement in cell adhesion through laser treatment with a biomimetic coating could be useful in reducing tissue damage at the prosthetic to bone junction and minimizing the loosening of prosthetics over time.