• Journal of Information Processing Systems
• /
• v.17 no.5
• /
• pp.1004-1019
• /
• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.2749-2763
• /
• 2021

The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is proposed to optimize the Retinex model. With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion outcome is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high contrast targets in IR images. Experiment statistic shows that compared to some advanced approaches, the proposed method has superiority on detail preservation and visual quality.

• Asia-Pacific Journal of Business
• /
• v.11 no.1
• /
• pp.93-114
• /
• 2020

Purpose -The purpose of this study is to analyze how the accreditation system affect the selection of business strategies in social enterprises, which create social value rather than maximize profits. Design/methodology/approach - This study collected survey data from 40 accredited and 53 non-accredited social enterprises. This research employs a Fuzzy-set/qualitative comparative analysis to compare the combinations of factors that affect a social enterprise's performance Findings - The results show that for accredited enterprises organizational capabilities are significantly more important than networking capabilities, whereas for non-accredited enterprises internal communication, governance capacities and networking competencies are most important capabilities to improving their social performance. And also The accreditation systems for social enterprises would entice social enterprise away from business strategies based on with local society, which is differentiated with commonly accepted social enterprise model. Research implications or Originality - This research suggests that the accreditation system for social enterprises should be redesigned for enticing social enterprises in Korea to be more localized to meet local needs in terms of positive changes of local society.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.319-331
• /
• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• Earthquakes and Structures
• /
• v.24 no.1
• /
• pp.1-9
• /
• 2023

A model for calculating structure interacted mechanics is proposed. A structural interaction model and controller design based on tuned mass damping (TMD) was developed to control the induced vibration. A key point is to introduce a new analytical model to evaluate the properties of the TMD that recognizes the motion-dependent nonlinear response observed in the simulations. Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. Firstly, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Furthermore, based on the relaxed method, the intelligent approach of stability criterion can be regulated appropriately and the artificial TMD was found to be effective in reducing cross-wind vibrations.

• Journal of Korean Port Research
• /
• v.11 no.2
• /
• pp.191-202
• /
• 1997

As the suitability of berth allocation will ultimately have a significant influence on the performance of a berth, a great deal of attention should be given to berth allocation. Generally, a berth allocation problem has conflicting factors between servers and users. In addition, there is uncertainty in great extent caused by various factors such as departure delay, inclement weather on route, poor handling equipment, a lack of storage space, and other factors contribute to the uncertainty of arrival and berthing time. Thus, it is necessary to establish berth allocation planning which reflects the positions of interested parties and the ambiguity of parameters. For this, a berth allocation problem is formulated by fuzzy 0-1 integer programming introducing the concept of maximum Position Shift(MPS). But, the above approach has limitations in terms of computational time and computer memory when the size of problem is increased. It also has limitations with respect to the integration of other sub-systems such as ship planning system and yard planning system. For solving such problem, this paper focuses particularly on developing an efficient heuristic algorithm as a new technique of getting an effective solution. And also the suggested algorithm is verified through the illustrative examples and empirical appalicaton to BCTOC.

• Journal of Korean Port Research
• /
• v.12 no.1
• /
• pp.1-8
• /
• 1998

If a system has a large boundary and complexity, forecast's accuracy will be very low when consider the system's substance as black box. Thus, it is necessary that analysis by structure model. To examine competition in Northeast Asia Ports, it has assumed that the form of structure model, For which the System Dynamics method is adapted in this paper. Northeast Asia Ports Model includes five ports - Pusan, Kobe, Yokohama, Kaoshiung, Keelung, - which are adjacent to each other by geographically and has a competition relation. The Northeast Asia Ports Model has several sub-systems which consists of each unit port models. And, each unit port model found by quantitive, qualititive factors and their feedback loops. All effects which components of one port have influence to components of the rest ports must be surveyed in order to construct Northeast Asia Ports Model, but it may be impossible currently. In this paper Northeast Asia Ports Model was simplified by HFP-Hierarchical Fuzzy Process Method-adapted to integration of level variables of unit port model. Container cargo volumes in Northeast Ports Model is distributed by results of HFP method. And distributed container cargo volumes effected to unit port model. Developed model can estimate change of container cargo volumes in competitive relation by alternation of simple parameter, and reflects dynamics characteristics which are included in model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1276-1295
• /
• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• Clean Technology
• /
• v.29 no.2
• /
• pp.145-150
• /
• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1888-1906
• /
• 2024

Aiming at the problems that the edge of melanoma image is fuzzy, the contrast with the background is low, and the hair occlusion makes it difficult to segment accurately, this paper proposes a model MSCNet for melanoma segmentation based on U-net frame. Firstly, a multi-scale pyramid fusion module is designed to reconstruct the skip connection and transmit global information to the decoder. Secondly, the contextural information conduction module is innovatively added to the top of the encoder. The module provides different receptive fields for the segmented target by using the hole convolution with different expansion rates, so as to better fuse multi-scale contextural information. In addition, in order to suppress redundant information in the input image and pay more attention to melanoma feature information, global channel attention mechanism is introduced into the decoder. Finally, In order to solve the problem of lesion class imbalance, this paper uses a combined loss function. The algorithm of this paper is verified on ISIC 2017 and ISIC 2018 public datasets. The experimental results indicate that the proposed algorithm has better accuracy for melanoma segmentation compared with other CNN-based image segmentation algorithms.