bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Generic Analytical Model of Fractance Response
Authors: Banchuin Rawid , Chaisricharoen Roungsan
Published: April 2020

Fractance is often cited in various engineering disciplines for example, analog circuits and systems, biomedical engineering, control engineering and electronic engineering etc. In this research, a novel generic analytical model of the voltage response of fractance in time domain has been proposed. This model composes of two parts for fractance with order ranges from 0 to 1 and larger than 1 respectively. With this model, the asymptotic and transient voltage responses of fractance can also be determined. Unlike the results of the previous works which are applicable only to fractance with certain orders under certain excitations, this model can be applied to fractance of arbitrary order under arbitrary type of periodic excitation. Moreover, it has been shown that the proposed model is in a realistic format. So, this model has been found to be beneficial to various fractance involved engineering disciplines stated above.
A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System
Authors: Sento Adna , Kitjaidure Yuttana
Published: April 2020

This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4- joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNPID controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.
A Copyright- and Privacy-Protected Diabetic Retinopathy Diagnosis Network
Authors: Sae-Tang Wannida
Published: April 2020

This paper proposes a copyright- and privacyprotected diabetic retinopathy (DR) diagnosis network. In the network, DR lesions are automatically detected from a fundus image by firstly estimating non-uniform illumination of the image, and then the lesions are detected from the balanced image by using level-set evolution without re-initialization. The lesions are subsequently marked by using contours. The lesion-marked fundus image is subsequently shared for intra or inter hospital network diagnosis with copyright and privacy protection. Watermarking technique is used for image copyright protection, and visual encryption is used for privacy protection. Sign scrambling of two dimensional (2D) discrete cosine transform (DCT) and one dimensional (1D) DCT is proposed for lesion-marked fundus image encryption. The proposed encryption methods are compared with other transform-based encryption methods, i.e., discrete Fourier transform (DFT) amplitude-only images (AOIs), DCT AOIs, and JPEG 2000-based discrete wavelet transform (DWT) sign scrambling which were proposed for image trading system. Since the encryption is done after DR diagnosis, contours used for DR marking must also be visually encrypted. The proposed encryption methods are effective for strong-edge images that are suitable for lesion-marked fundus images, while random sign-based JPEG 2000, DFT AOIs, and DCT AOIs encrypt the images imperfectly. Moreover, the proposed methods are better in terms of image quality. In addition, watermarking performance and compression performance are confirmed by experiments.
Probabilistic Modelling of Variation in FGMOSFET
Authors: Banchuin Rawid , Chaisricharoen Roungsan
Published: April 2020

The analytical probabilistic modelling of random variation in the drain current of a Floating-Gate MOSFET (FGMOSFET) induced by manufacturing process variations has been performed. Both triode and saturation region operated FGMOSFETs have been considered. The results have been found to be very efficient since they can accurately fit the probabilistic distributions of normalized random drain current variations of the candidate triode and saturation FGMOSFETs obtained using the 0.25μm level BSIM3v3 based Monte-Carlo SPICE simulations, where the variation of the saturation FGMOSFET has been found to be more severe. These results also satisfy the goodness of fit test at a very high level of confidence and more accurately than the results of the previous probabilistic modelling attempts. Using our results, many statistical parameters, probabilities and the objective functions, which are useful in statistical/variability aware analysis and design involving FGMOSFETs can be formulated. The impact of drain current variation upon the design trade-offs can be studied. It has been found that the occurrence of the drain current variation is absolutely certain. Moreover, the analytical probabilistic modelling and computationally efficient statistical/ variability aware simulation of FGMOSFET based circuits can also be performed.
Studying Peripheral Vascular Pulse Wave Velocity Using Bio-impedance Plethysmography and Regression Analysis
Authors: Huang Ji-Jer , Huang Yang-Min , See Aaron Raymond
Published: April 2020

In this study, a simple bioimpedance plethysmography method was employed to measure the pulse wave velocity (PWV) from the radial artery in the wrist to the middle finger of a patient. Subsequently, electrocardiography was combined with a bioimpedance method to calculate the PWV from ECG and pulse waves to the middle finger. Experiments were conducted by employing cuffs that temporarily block blood flow to produce observable changes in the PWV. Statistical results indicated that temporary blockage of blood flow did not influence the PWV of typical healthy people. Moreover, multiple regression analysis was used to establish an equation for estimating two types of PWV and their relevance with other physiological parameters. Multiple regression analysis indicated that the abdomen circle and height are independent predictors of the PWV from the radial artery in the wrist to the middle finger (wfPWV) (r = 0.893). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) are independent predictors of the PWV from the EGC T wave to the middle finger (tfPWV) (r = 0.898). Correlation analysis showed the wfPWV is significantly associated with tfPWV (r = 0.770, p < 0.01).
An Efficient Search Algorithm for Multi-hop Network Localization in a Sparse Unit Disk Graph Model
Authors: Kaewprapha Phisan , Tansarn Thaewa , Puttarak Nattakan
Published: April 2020

We consider a network localization problem by modeling this as a unit disk graph where nodes are randomly placed with uniform distribution in an area.The connectivity between nodes is defined when the distances fall within a unit range. Under a condition that certain nodes know their locations (anchor nodes), this paper proposes a heuristic approach to find a realization for the rest of the network by applying a tree search algorithm in a depth- first search manner. Our contribution is to put together a priori information and constraints such as graph properties in order to speed up the search. An  evaluation function is formed and used to prune down the search space. This evaluation function is used to select the order of the unknown nodes to iterate. This paper also extends the idea further by accommodating a variety of other properties of graphs into the evaluation function. The results show that node degrees, node distances and shortest paths to anchor nodes drastically reduce the number of iterations required for realizing a feasible localization instance both in noise-free and noisy environments. Finally, some preliminary complexity analysis is also given.
RCA on FPGAs Designed by RTL Design Methodology and Wave-Pipelined Operation
Authors: Sato Tomoaki , Chivapreecha Sorawat , Moungnoul Phichet , Higuchi Kohji
Published: April 2020

Field-programmable gate arrays (FPGAs) are used in various systems with reconfigurable functions. Conventional FPGAs have been developed using a transistor level description for minimizing routing delay. Although FPGAs developed with a register transfer level (RTL) design methodology provide various benefits to the designers of a system-on-a-chip (SoC), they have not been realized. Therefore, the authors advanced their development. They should be shown to operate with practical throughput. For this purpose, circuits on these device need to be designed and evaluated. In this paper, a ripple-carry adder (RCA) was designed and the throughput of the RCA was evaluated. The resulting throughput was applicable to network processors. Additionally, a wave-pipelined operation without changing the RCA revealed that the problem of routing delay in FPGA developed by RTL methodology was mitigated. The contributions of this paper are to clarify that a 4-bit adder can be implemented on FPGAs and their throughput can be improved by wave-pipelined operations.
The improvement of a quality assurance information systems Based Six-Sigma Principles
Authors: Cheosuwan Thitirath , Saipanya Somporn , Tongphet Supan
Published: April 2020

This paper presents the development of a quality assurance information system based on Six-Sigma principles for schools in northern Thailand. The aim of this research is to solve problems that occurred in a previous system. These included an awkward user interface, support without quality standards, user confusion in the workflow, along with inaccurate and unreliable information system processes. Six-Sigma uses five processes. These are the define, measure, analyze, improvement, and control phases (DMAIC). According to previous studies, measurement of user satisfaction and expectations has three components: standards-based service, system reliability, and processing speed. Technical and non-technical quality measurements indicated that the initial system operated at a 2-sigma level. These measurements confirm that the proposed system provides the improved services over the initial system and that it effectively solved many of its problems. Moreover, it raised the efficiency of the service system based on quality assurance standards for the mission of the schools in the northern region.
Enhanced Running Spectrum Analysis for Robust Speech Recognition Under Adverse Conditions: A Case Study on Japanese Speech
Authors: Mufungulwa George , Tsutsui Hiroshi , Miyanaga Yoshikazu , Abe Shin-ichi
Published: April 2020

In any real environment, noises degrade the performance of Automatic Speech Recognition (ASR) systems. Additionally, in the case of similar pronunciations, it is not easy to realize a high accuracy of recognition. From  this point of view, our work envisions an enhanced algorithm processing a speech modulation spectrum, such as Running Spectrum Analysis (RSA). It was also adequately applied to observed speech data. In the envisioned method, a modulation spectrum filtering (MSF) method directly modified the observed cepstral modulation spectrum by a Fourier transform of the cepstral time frequency. The method and experiments carried out for various passbands had favorable results that showed an improvement of about 1-4 % in recognition accuracycompared to conventional methods.
Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization
Authors: Patnaik Yogananda , Patra Dipti
Published: April 2020

Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To  overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation  results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%
Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist
Authors: Hiraoka Daiki , Ito Momoyo , Ito Shin-ichi , Fukumi Minoru
Published: April 2020

We propose a method which can discriminate hand motions in this paper. We measure an electromyogram (EMG) of wrist by using 8 dry type sensors. We focus on four motions, such as “Rock-Scissors-Paper” and “Neutral”. “Neutral” is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove a hum noise. Next, we combine values of sensors based on a Gaussian function. In this Gaussian function, variance and mean are 0.2 and 0, respectively. We then apply normalization by linear transformation to the values. Subsequently, we resize the values into the range from -1 to 1. Finally, a support vector machine (SVM) conducts learning and discrimination to classify them. We conducted experiments with seven subjects. Average of discrimination accuracy was 89.8%. In the previous method, the discrimination accuracy was 77.1%. Therefore, the proposed method is better in accuracy than the previous method. In future work, we will conduct an experiment which discriminates Japanese Janken of a subject who is not learned.
An Empirical Evaluation of the Parameters of Trilateral Filter for Noise Removal Implementation on Gaussian and Impulsive Noise
Authors: Patanavijit Vorapoj , Thakulsukanant Kornkamol
Published: April 2020

Since 1998, the Bilateral filter (BF) is worldwide accepted for its performance in practical point of view under Gaussian noise however the Bilateral filter has a poor performance for impulsive noise. Based on the combining of the Rank-Ordered Absolute Differences (ROAD) detection technique and the Bilateral filter for automatically reducing or persecuting of impulsive and Gaussian noise, this Trilateral filter (TF) has been proposed by Roman Garnett et al. since 2005 but the Trilateral filter efficiency is rest absolutely on spatial, radiometric, ROAD and joint impulsivity variance. Hence, this paper computationally determines the optimized values of the spatial, radiometric, ROAD and joint impulsivity variance of the Trilateral filter (TF) for maximum performance. In the experiment, nine noisy standard images (Girl-Tiffany, Pepper, Baboon, House, Resolution, Lena, Airplain, Mobile and Pentagon) under both five power-level Gaussian noise setting and five density impulsive noise setting, are used for estimating optimized parameters of Trilateral filter and for demonstrating the its overall performance, which is compared with classical noise removal techniques such as median filter, linear smoothing filter and Bilateral filter (BF). From the noise removal results of empirically experiments with the highest PSNR criterion, the trilateral filter with the optimized parameters has the superior performance because the ROAD variance and joint impulsivity variance can be statistically analyzed and estimated for each experimental case.
Similarity Score Estimation and Gaps Trimming of Multiple Sequence Alignment for Phylogenetic Tree Analysis
Authors: Damkliang Kasikrit , Tandayya Pichaya , Sangket Unisa , Pasomsub Ekawat
Published: April 2020

Phylogenetic tree analysis is a process for finding the highest possible revolution tree history of an interested organism. The important step of the process is multiple sequences alignment (MSA) which is operated using any MSA tool that produces a result in blocks of the Phylip format. Bioinformaticians have to manually determine and trim gaps of the MSA blocks using relevant tools of a software package in the off-line mode. The data blocks need to be manually cut-and-pasted between these tools. This working steps tend to be error-prone and time consuming. In addition, improper algorithm selection for tree inferring without applying an MSA similarity score tends to generate the phylogenetic tree with low accuracy and also take much more time. In this work, we present a new practical approach for the phylogenetic tree analysis applying our enhancement for the similarity score estimation and gaps trimming of the MSA blocks. We propose \textit{in-silico} algorithms for automating the concerned similarity score estimation and gaps trimming, and deploy them as web services. We demonstrate the web services utilized by composing them into an integrated stateful WSDL workflow. Our case study datasets are a complete coding sequences (CDS) and sets of complete genome of Dengue Viruses - 2, fetched from the NCBI RefSeq nucleotide database. Our proposed algorithms have correctly returned results, verified and satisfied by our  bioinformaticians. Our distributions, user manuals and endpoints of the web services, and the open source programs are available at https://bioservices.sci.psu.ac.th.
Health Risk Analysis Expert System for Family Caregiver of Person with Disabilities using Data Mining Techniques
Authors: Suksawatchon Ureerat , Suksawatchon Jakkarin , Lawang Wannarat
Published: April 2020

The nursing care for the family caregiver of the disabled person is an important task for long-term care, since the caring people with disabilities is the difficult and hard task. In this paper, the Health Risk Analysis System or HRAS is introduced which is the new expert system for identifying the health risk level in three aspects including mental, physical, and social health aspects, and provides the intervention according to the health risk level of each aspect as well. The HRAS is the client-server system. The HRAS client proceeds on web-based application to collect health data via online questionnaire and shows the analysis results. The collected health data are transmitted to the server to analysis and to assess the health risk level by using the proposed classifier model named Risk Analysis Classifier or RAC. The classification algorithm and rule-based classifier are used to build the RAC. The RAC is evaluated using k-fold cross validation and the experts with annotated health data and unseen data. The evaluation results showed that Neural Network performs the best performance overall which it achieves the accuracy above 90% in all health data sets. Thus, the Neural Network is the most suitable classifier for this work. In addition, the HRAS has been deployed and collected the user experience via the formal survey. These survey results demonstrated that the system provides high accuracy assessment and very utilization in several aspects.
Analysis of Circularly Polarized Light Irradiation Effects on Double Ferromagnetic-Gate Silicene Junction
Authors: Chantngarm Peerasak , Yamada Kou
Published: April 2020

We present an analytical study of effects that off-resonant circularly polarized light irradiation has on spin-valley currents in dual ferromagnetic-gate silicene-based junctions. Two identical electric fields are applied to both ferromagnetic (FM) gates. Two types of exchange field configurations, parallel (P) and anti-parallel (AP), are applied along with chemical potential to the FM gates in this investigation. The results show that application of circularly polarized light has an impact on polarized spin and valley current characteristics, particularly at the off-resonant frequency region. It also enhances the amplitude of tunnelling magnetoresistance (TMR) significantly. In addition, we found that exchange field configuration has an effect on both spin polarization and valley polarization. Our study reveals that light intensity plays the main role on the light irradiation effects, where the band structure and change electronic properties of the materials are modified by photon dressing to create a new phase of electronic structure. The change of band structure in each region affects the transmission coefficients and transmission probability amplitude of electrons, which in turn affects the conductance of each spin-valley current component. Our study suggests the potential of this scheme in applications, such as spin-valleytronic photo-sensing devices under polarized-photo irradiation.
SSD Bandwidth Distributing I/O Scheduler Considering Garbage Collection
Authors: Park Jung Kyu , Kim Jaeho
Published: April 2020

There were scheduler studies for QoS(Quality of Service) or SLA(Service Level Agreement) of hard disks. The use of SSDs as storage has been increasing dramatically in recent systems due to their fast performance and low power usage. However, the studies to guarantee the SLA are based on the hard disk and do not consider SSD which is a ash storage device. In the SSD, GC(Garbae Collection) process copies data to an empty block and the corresponding block is removed by the GC. This causes SSD performance to degrade in a virtualized environment with many I/Os. We considered the Linux scheduler to take SSD characteristics into consideration and to improve I/O performance. In this paper, we propose a MTS-CFQ I/O scheduler that is implemented by modifying the existing Linux CFQ I/O scheduler. Our proposed method controls the time slice based on the I/O bandwidth for the current storage device. Real workload-driven simulation based experimental results have shown that MTS-CFQ can improve performance by up to 20% with an average of 5%, compared with the traditional CFQ I/O for the four workload considered.
Comparative Study of Knee-Based Algorithms for Many-Objective Optimization Problems
Authors: Alzahrani Saad M. , Wattanapongsakorn Naruemon
Published: April 2020

Nowadays, most real-world optimization problems consist of many and often conflicting objectives to be optimized simultaneously. Although, many current Multi-Objective optimization algorithms can efficiently solve problems with 3 or less objectives, their performance deteriorates proportionally with the increasing of the objectives number. Furthermore, in many situations the decision maker (DM) is not interested in all trade-off solutions obtained but rather interested in a single optimum solution or a small set of those trade-offs. Therefore, determining an optimum solution or a small set of trade-off solutions is a difficult task. However, an interesting method for finding such solutions is identifying solutions in the Knee region. Solutions in the Knee region can be considered the best obtained solution in the obtained trade-off set especially if there is no preference or equally important objectives. In this paper, a pruning strategy was used to find solutions in the Knee region of Pareto optimal fronts for some benchmark problems obtained by NSGA-II, MOEA/D-DE and a promising new Multi-Objective optimization algorithm NSGA-III. Lastly, those knee solutions found were compared and evaluated using a generational distance performance metric, computation time and a statistical one-way ANOVA test.
Cell Throughput based Sleep Control Scheme for Heterogeneous Cellular Networks
Authors: Phaiwitthayaphorn Prapassorn , Mori Kazuo , Kobayashi Hideo , Boonsrimuang Pisit
Published: April 2020

The mobile traffic continuously grows at a rapid rate driven by the widespread use of wireless devices. Along with that, the demands for higher data rate and better coverage lead to increase in power consumption and operating cost of network infrastructure. The concept of heterogeneous networks (HetNets) has been proposed as a promising approach to provide higher coverage and capacity for cellular networks. HetNet is an advanced network consisting of multiple kinds of base stations, i.e., macro base station (MBS), and small base station (SBS). The overlay of many SBSs into the MBS coverage can provide higher network capacity and better coverage in cellular networks. However, the dense deployment of SBSs would cause an increase in the power consumption, leading to a decrease in the energy efficiency in downlink cellular networks. Another technique to improve energy efficiency while reducing power consumption in the network is to introduce sleep control for SBSs. This paper proposes cell throughput based sleep control which the cell capacity ratio for the SBSs is employed as decision criteria to put the SBSs into a sleep state. The simulation results for downlink communications demonstrate that the proposed scheme improves the energy efficiency, compared with the conventional scheme.
The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem
Authors: Phu-ang Ajchara , Jitkongchuen Duangjai
Published: April 2020

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and  embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.
Synthetic Minority Over-Sampling for Improving Imbalanced Data in Educational Web Usage Mining
Authors: Intayoad Wacharawan , Kamyod Chayapol , Temdee Punnarumol
Published: April 2020

Educational data mining is the method for extracting and discovering new knowledge from education data. As education data is often complex and imbalanced, it requires a data preprocessing step or learning algorithms in order to obtain accurate analysis and interpretation. Many studies emphasize on classification and clustering methods in order to get insight and comprehensive knowledge from education data. However, a small number of previous works exclusively focused on the preprocessing of education data, particularly on the topic of the imbalanced dataset. Therefore, this research objective is to enhance the accuracy of data classification in educational web usage data. Our study involves the application of synthetic minority over-sampling techniques (SMOTE) to preprocess the raw dataset from web usage data. The minority class is a group of the students who failed the examination and the majority class is the students who passed the examination. In our experiments, four synthetic minority over-sampling methods are applied, SMOTE, and its variants: Borderline-SMOTE1, Borderline-SMOTE2, and SVM-SMOTE, in order to balance the number of samples in the minority class. The experiments are evaluated by comparing the results from well-known classification methods that are Naive Bayesian, decision tree, and k-nearest neighbors. The study experiments with real-world datasets from education data. The results present that synthetic minority over-sampling methods are capable of improving the detection of the minority class and achieve improving classification performance on precision, recall, and F1-value.
Ed
Criteria Redesign for Student Loan Consideration Using Factor Analysis and Data Clustering Approach
Authors: Chaiwut Klangwaree , Rueangsirarak Worasak , Chaisricharoen Roungsan
Published: April 2020

This paper presents the importance of redesigning the student loan consideration criteria which had been revealed to have some fault in evaluating the candidates. The historical data of student loan candidates elicited from their application form in the 2016 academic year was collected and analyzed by using Factor Analysis. There are 507 samples with 17 information attributes. The factor analysis reduced the dimensions of the variance in the samples by identifying the discriminative factors for student loan consideration. The experimental result shows that only nine factors were identified as discriminative factors, which are 1) Part-time job taken by the student, 2) Other scholarships that the student had been receiving, 3) Father’s salary, 4) Family ownership of the land, 5) House rental expense, 6) Number of siblings in the family, 7) Number of siblings currently studying, 8) Amount of money that the student get from other scholarships, and 9) Parental Marital Status. The clustering technique was used to measure the group of important factors reduced from the factor analysis. The clustering result showed that the clusters are obviously separated from each other. Therefore, these discriminative factors were elicited by using factor analysis which can be used to reconstruct the student loan consideration criteria and implement a decision support system.
NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks
Authors: Termritthikun Chakkrit , Muneesawang Paisarn
Published: April 2020

The growth of high-performance mobile devices has resulted in more research into on-device image recognition. The research problems have been the latency and accuracy of automatic recognition, which remain as obstacles to its real-world usage. Although the recently developed deep neural networks can achieve accuracy comparable to that of a human user, some of them are still too slow. This paper describes the development of the architecture of a new convolutional neural network model, NU-LiteNet. For this, SqueezeNet was developed to reduce the model size to a degree suitable for smartphones. The model size of NU-LiteNet was therefore 2.6 times smaller than that of SqueezeNet. The model outperformed other Convolutional Neural Network (CNN) models for mobile devices (eg. SqueezeNet and MobileNet) with an accuracy of 81.15% and 69.58% on Singapore and Paris landmark datasets respectively. The shortest execution time of 0.7 seconds per image was recorded with NU-LiteNet on mobile phones.
A Review on Stereo Vision Algorithm: Challenges and Solutions
Authors: Kok Kai Yit , Rajendran Parvathy
Published: April 2020

This paper presents a survey on existing stereo vision algorithms. The existing stereo vision algorithms are discussed in terms of concept, performance and related improvements. Also, a brief analysis of performance comparison among existing stereo vision algorithms is presented. Moreover, available improvements and solutions for stereo vision challenges such as computational complexity, occlusion, radiometric distortion, depth discontinuity and textureless region are reviewed.
Analysing the EEG Signal Effectiveness of Chiang Rai Arabica Drip Coffee on Individual Human Brainwave
Authors: Chenghu Cui , Wicha Santichai , Chaisricharoen Roungsan
Published: April 2020

This study focused on the impact of local Arabica coffee on the level of attention of individual brain waves, and how coffee affects Human EEG Frequency. Local Arabica coffee is adopted in this study as a medium to wake up the Beta wave. The Personal brainwave data is then recorded through EEG equipment and classified. The result showed that local coffee is helping to improve people's attention level — the study conducted on fifty participants: twenty-five males and twenty-five females aged between twenty to thirty years old. Brainwaves or Electroencephalography are collected twice before and after drinking coffee to compare the effects of Arabica on human brain waves by using NeuroSky mindwave mobile. The paired sample t-test test was employed for comparing two groups of Beta brainwaves experiment. Besides, the k-means algorithm is used to perform data mining on brain waves, and the differential brain wave signal data is clustered and divided into three levels. The experimental results showed that there was a statistically significant difference between the two paired samples. Therefore, the results confirmed that local Arabica coffee has a direct impact on personal attention.
Improving model using estimate error for daily inflow forecasting
Authors: Ngamsanroaj Yaowalak , Tamee Kreangsak
Published: April 2020

Inflow forecasting is one of the important components for reservoir operation and resource management. To obtain enhanced accuracy for forecasting reservoir inflow, this paper proposed an improved model for forecasting the inflow of Bhumibol reservoir. The 3,169 records of daily inflow data from June 1, 2008, to February 1, 2017, had been collected to calculate the inflow into the reservoir by using Artificial Neural Networks (ANN) Back-Propagation Learning Algorithm for forecasting the inflow of the reservoir in the main model and error prediction model. The performance of the model can be evaluated by four methods: the coefficient of determination (R2), the Nash-Sutcliffe efficiency (NSE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Thus, these proposed main and error prediction models were combined to support the forecast of reservoir inflow. The performance of the proposed model can be evaluated by following measured values: R2 was 0.927, NSE was 0.925, RMSE was 6.805 and MAE was 3.611. This indicates that the improved model provides more accurate value.
Acquisition and Utilization of Mental Imagery Capability in Robotic Action Sequencing Tasks
Authors: Seepanomwan Kristsana
Published: April 2020

This work presents a series of neurorobotic models underlying learning in robots. It demonstrates the way in which, during sensorimotor exploration, robots do not just gain knowledge about how to form movement primitives but also obtain a mental imagery capability. Mental imagery plays a key role in these computational models by accelerating learning processes of action sequencing tasks. The first experiment involves permitting a humanoid robot to learn how to retrieve an out-of-reach object using a provided tool. This experiment explores a phenomenon on tool use development found in human infants. In addition, it tests two hypotheses on tool use development. The second experiment extends the domain of robot learning by targeting a useful robotic application. It drives a service robot to learn to acquire knowledge of how to manipulate perceived objects based on the objects’ information and a goal from users. By means of planning, learning the sequence of actions in mind, the robots are able to learn by examining actions’ outcome without really performing actions. This allows the second model to completely exclude parts of overt movements from the training loop. The results confirm that two types of robots can complete their given tasks in a reasonable period of time. The proposed models would benefit robotic applications in terms of online learning.
An integration of TAM with usage barriers and ability to understand consumers intention to use SSTs
Authors: Tongnamtiang Sompoch , Leelasantitham Adisorn
Published: April 2020

This study examined the factors that influence consumers’ acceptance and use of self-service technology by integrating a ground theory model such as the technology acceptance model and the theory of innovation adoption. The variables that affect consumers’ attitudes and intentions were investigated; this included perceived usefulness, perceived ease of use, relative advantage, complexity and knowledge. Further, the variables from the resistance theory, which consist of usage barriers and ability, were investigated. Interviews and surveys were conducted with two business operators that provide TVM services; eight hundred online and offline consumers who experienced the TVMs were selected as a sample. The research model of this study was developed, and the correlations between the variables analyzed, using structural equation modeling. Our results confirm that the factors such as relative advantage, knowledge, perceived usefulness and perceived ease of use positively affected the attitude and intention toward using the TVM; factors such as usage barriers and complexity negatively impacted the intention to use the top-up machine. These results can be used as baseline data for developing or designing self-service technology. Entrepreneurs should consider these factors to make technology continue to be recognized and persisted in the use of technology.
Process tree-based analysis method of DECLARE relation constraints in acyclic bridge-less well-structured workflow nets
Authors: Yamaguchi Shingo , Ahmadon Mohd Anuaruddin Bin
Published: April 2020

In this paper, we proposed a method to analyze workflows’ constraints whose templates are defined in a declarative language called DECLARE. Checking such constraints is important but known to be intractable in general. Our results show three things. First, utilizing a tree representation of workflow process called {\it process tree}, we provided necessary and sufficient conditions on the constraints. Second, those conditions enable us to not only check a given constraint in polynomial time but also find a clue for improving the net if it violates the constraint. Third, we revealed the relationship among the constraint templates.
An Enhanced ABC algorithm to Solve the Vehicle Routing Problem with Time Windows
Authors: Kantawong Krittika , Pravesjit Sakkayaphop
Published: April 2020

This work proposes an enhanced artificial bee colony algorithm (ABC) to solve the vehicle routing problem with time windows (VRPTW). In this work, the fuzzy technique, scatter search method, and SD-based selection method are combined into the artificial bee colony algorithm. Instead of randomly producing the new solution, the scout randomly chooses the replacement solution from the abandoned solutions from the onlooker bee stage. Effective customer location networks are constructed in order to minimize the overall distance. The proposed algorithm is tested on the Solomon benchmark dataset where customers live in different geographical locations. The results from the proposed algorithm are shown in comparison with other algorithms in the literature. The findings from the computational results are very encouraging. Compared to other algorithms, the proposed algorithm produces the best result for all testing problem sets. More significantly, the proposed algorithm obtains better quality than the other algorithms for 39 of the 56 problem instances in terms of vehicle numbers. The proposed algorithm obtains a better number of vehicles and shorter distances than the other algorithm for 20 of the 39 problem instances.
Model of Automatic Classification and Localization of Images
Authors: Serebryanaya L. V. , Bochkarev K. Y. , Popitich A. Y.
Published: April 2020

The work is devoted to the identification of images in pictures, which is performed as a result of the classification and localization procedures. Analysis of models, methods and algorithms has shown that for solving the set task it is preferable to use machine learning, an artificial neural network and a genetic algorithm. The architecture of a convolutional artificial neural network is proposed. It can solve both the problem of classification and the problem of localizing images. First the network is trained, then a class is determined for the image fed to its input. Objects are localized in the image at the final stage of operations of the convolutional neural network. For this, the output values of the penultimate layer of the model are analyzed, after which the layers are traversed in the reverse order. Its goal is to find the regions with the highest response on the source image. The combined model showed acceptable results both in classification and in localization of objects. All parameters for the network are determined automatically using a genetic algorithm. Further improvement of the proposed model results will be performed by implementing distributed computing on it.
Model of Reconfigurable Steganographic System Using Blockchain Technology
Authors: Timoschenko E. V. , Razhkov A. F.
Published: April 2020

An effective method for solving data security problems is proposed. A new approach to the design of steganographic system based on the three basic methods - the introduction of parts of a shared secret into images with the possibility of further extraction of secret information, authentication of parts of a secret, self-healing of this security system. Such a system provides a greater degree of protection and secure data storage. The Windows application implements the proposed steganographic model, has no analogues and meets all the requirements set for steganographic software.
Blended Distance Learning: Search for an Optimal Format
Authors: Tsarenko I. V.
Published: April 2020

Article is devoted to a search for an optimal format of blended distance learning. The questioning of distance learning students of Sukhoi State Technical University of Gomel was used as a main research method. One discipline from the academic learning program was studied using e-learning technology (on-line classes) whereas (while) others disciplines were studied using traditional classroom face-to-face learning technique. Thus, students using blended distance learning have opportunity to compare and estimate efficiency together with advantages of both educative techniques. On-line classes were held in the period between sessions using BigBlueButton program.

It was found that student majority estimate blended distance learning as most efficiency learning technique; half of questioning students consider that the quality of face-to-face and on-line learning is equal. The main advantages and disadvantages of e-learning were analyzed. Highest student attendance of on-line classes between sessions in comparison with face-to-face classes during session time was found. Optimal amount of disciplines for on-line learning to organize blended distance learning were established. Best regime, time and intensity for on-line classes were determined. Results of these investigations may be useful to organize optimal format of blended distance learning as well as to choose subjects for on-line learning and to create comfortable schedule of distance classes.
RECONHECIMENTO DO PROFISSIONAL DE EDUCAÇÃO FÍSICA PELO CONSELHO NACIONAL DE SAÚDE: INTERVENÇÃO RECOMENDAÇÕES SOBRE CONDUTAS E PROCEDIMENTOS NA ATENÇÃO BÁSICA À SAÚDE
Authors: Hartmann Cassio , César Dias Lopes Gabriel
Published: April 2020

O presente estudo teve como objetivo, apresentar para os profissionais de Educação Física que trabalham com a saúde coletiva, ás  reas e níveis de intervenção do profissional de Educação Física e as recomendações e condutas na Atenção Básica á Saúde. O  onselho Nacional de Saúde, reconhece o profissional da Educação Física sendo da área de saúde, desde 06 de março de 1997,  través  a Resolução nº 218. De acordo com a Portaria 154/2008, as profissões que poderão compor os NASF (NÚCLEO DE SAÚDE DA  AMÍLIA) são: Médico, Acupunturista, Assistente Social, Profissional de Educação Física, Farmacêutico, Fisioterapeuta, Fonoaudiólogo, Médico Ginecologista, Médico Homeopata, Nutricionista, Médico Pediatra, Psicólogo, Médico Psiquiatra e Terapeuta Ocupacional.  ste artigo justifica-se pela necessidade e carência de informações sobre a inserção do profissional de Educação Física na saúde  oletiva. caracteriza-se por pesquisa qualitativa aquela que identifica fatores relevantes de um objeto de maneira a analisar o teor de  ocumentos ou realidades, e ainda ressaltam a importância do embasamento teórico, assim sendo, trata-se de uma revisão  ibliográfica acompanhada por uma pesquisa qualitativa aos documentos que regulamentam e legitimam a atuação do Profissional  e Educação Física no sistema único de saúde e suas ações transversais. Pode-se concluir, que é de suma importância a intervenção  o profissional de Educação Física no Programa Saúde da Família (PSF) e no NASF (NÚCLEO DE SAÚDE DA FAMÍLIA) nos três níveis de intervenção (primária, secundária e terciária), atuando na promoção da saúde e na prevenção de doenças, diretamente no  ratamento não farmacológico e intervindo nos fatores de risco.
2.1. Гармонизация российской и китайской систем бухгалтерского учета и отчетности
Authors: Amurskaya M.A. , Amurskaya M.A. , Amurskaya M.A.
Published: April 2020

Продолжающиеся в настоящее время в Российской Федерации процессы реформирования системы учета обуславливают необходимость формирования такой национальной учетной системы, которая соответствовала бы особенностям развития национальной экономики и, одновременно с этим, гармонировала бы с национальными системами учета других стран, в частности, стратегическими экономическими партнерами. Среди таких стран особый научный и практический интерес представляет рассмотрение учетной системы Китайской Народной Республики на предмет возможности формирования конвергентного пути развития учетных систем России и Китая. В данной работе автор рассматривает исторические особенности формирования учетных систем России и Китая определяет место российской и китайской систем в мировой классификации раскрывает основные особенности нормативного и методологического регулирования учетных систем РФ и Китая проводит анализ основных форм бухгалтерской финансовой отчетности двух стран. В заключение автор обозначает основные механизмы гармонизации учетных систем России и Китая на современном этапе.

The ongoing processes of reforming the accounting system in Russia necessitate the formation of such a national accounting system that would be consistent with the development of the national economy and, at the same time, would be in harmony with national accounting systems of other countries, in particular strategic economic partners. Among such countries, special scientific and practical interest is the consideration of the accounting system of the PRC for the possibility of forming a convergent path of development of the accounting systems of Russia and China. In this paper, the author examines the historical features of the formation of accounting systems of Russia and China determines the place of the Russian and Chinese systems in the world classification reveals the main features of regulatory and methodological regulation of accounting systems of Russia and China analyzes the main forms of accounting financial statements of the two countries. In conclusion, the author identifies the main mechanisms of harmonization of accounting systems of Russia and China at the present stage.
5.5. ФИНАНСОВАЯ УСТОЙЧИВОСТЬ БАНКОВ: ЗАРУБЕЖНЫЕ И РОССИЙСКИЕ ПОДХОДЫ К ОЦЕНКЕ
Authors: Morozov A.V. , Morozov A.V. , Morozov A.V. , Morozov A.V. , Morozov A.V.
Published: April 2020

В статье рассмотрены подходы международных и национальных надзорных органов к оценке финансовой устойчивости банков и банковского сектора. Автором показано, что подходы международных надзорных органов базируются на сочетании микропруденциального и макропруденциального анализа и включают комплексную оценку основных финансовых показателей и банковских рисков. Подходы Центрального банка РФ опираются на международный опыт и направлены на обеспечение устойчивости банков к системным рискам в финансовом секторе экономики.

The article discusses the approaches of international and national supervisory authorities to assess the financial stability of banks and the banking sector. The author shows that the approaches of international supervisory authorities are based on a combination of microprudential and macroprudential analysis and include a comprehensive assessment of key financial indicators and banking risks. The approaches of the Central Bank of the Russian Federation are based on international experience and are aimed at ensuring the resilience of banks to systemic risks in the financial sector of the economy.