Earticle

상수관망 최적설계는 사용자가 설정한 목적에 따라 다양하게 사용된다. 상수관망 최적설계는 비용의 최소화 및 관의 제작 시 발생하는 에너지 최소화 등 목적이 다양하게 존재한다. 본 연구에서는 Modified Hybrid Vision Correction Algorithm (MHVCA)을 기반으로 다양한 상수관망에 대한 비용 최적설계를 진행하였다. 또한 새로운 평가지표인 Best Rate (BR)를 제안하였다. BR은 K-mean Clustering Algorithm을 기반으로 개발된 평가지표이다. BR을 통해 상수관망 최적설계에 사용된 각 알고리즘의 최적 설계안 탐색 가능성에 대한 비교를 하였다. 다양한 관망에 대한 MHVCA의 최적설계 결과를 Vision Correction Algorithm (VCA) 및 Hybrid Vision Correction Algorithm (HVCA)과 비교하였다. MHVCA는 VCA 및 HVCA보다 낮은 비용의 설계안을 탐색하였다. 또한 MHVCA는 낮은 비용의 설계안을 탐색할 확률이 VCA 및 HVCA보다 높았다. MHVCA는 본 연구에서 적용한 비용 최소화를 위한 상수관망 최적설계 뿐만이 아닌 다양한 목적을 위한 상수관망 최적설계에 적용할 경우 좋은 결과를 나타낼 수 있을 것이다.

The optimal design of Water Distribution System (WDS) is used in various ways according to the purpose set by the user. The optimal design of WDS has various purposes, such as minimizing costs and minimizing energy generated when manufacturing pipes. In this study, based on the Modified Hybrid Vision Correction Algorithm (MHVCA), a cost-optimal design was conducted for various WDSs. We also propose a new evaluation index, Best Rate (BR). BR is an evaluation index developed based on the K-mean Clustering Algorithm. Through BR, a comparison was made on the possibility of searching for the optimal design of each algorithm used in the optimal design of WDS. The results of MHVCA for WDS were compared with Vision Correction Algorithm (VCA) and Hybrid Vision Correction Algorithm (HVCA). MHVCA showed a lower cost design than VCA and HVCA. In addition, MHVCA showed better probability of lower cost designs than VCA and HVCA. MHVCA will be able to show good results when applied to the optimal design of WDS for various purposes as well as the optimal design of WDS for cost minimization applied in this study.

Laser interferometry is widely used as a measuring system in many fields because of its high resolution and its ability to measure a broad area in real-time all at once. In conventional laser interferometry, for example out-of-plane ESPI (Electronic Speckle Pattern Interferometry), in plane ESPI, shearography and holography, it uses PZT or other components as a phase shift instrumentation to extract 3-D deformation data, vibration mode and others. However, in most cases PZT has some disadvantages, which include nonlinear errors and limited time of use. In the present study, a new type of laser interferometry using a laser diode is proposed. Using Laser Diode Sinusoidal Phase Modulating (LD-SPM) interferometry, the phase modulation can be directly modulated by controlling the laser diode injection current thereby eliminating the need for PZT and its components. This makes the interferometry more compact. This paper reports on a new approach to the LD (Laser Diode) Modulating interferometry that involves four-frame phase shift method. This study proposes a four-frame phase mapping algorithm, which was developed to have a guaranteed application, to stabilize the system in the field and to be a user-friendly GUI. In this paper, the theory for LD wavelength modulation and sinusoidal phase modulation of LD modulating interferometry is shown. Using modulating laser and research of measurement algorithm does comparison with existent ESPI measurement algorithm. Algorithm measures using GPIB communication through most LabVIEW 8.2. GPIB communication does alteration through PC. Transformation of measurement object measures through modulating laser algorithm that develops. Comparison of algorithm of modulating laser developed newly with existent PZT algorithm compares transformation price through 3-D. Comparison of 4-frame phase mapping, unwrapping, 3-D is then introduced.

상수관망의 최적설계는 절점의 최소 요구 수압을 만족함뿐만 아니라 관로비용의 최소화 등을 목적으로 한다. 상수관망 설계안의 수는 다양한 관의 배치로 인해 기하급수적으로 증가한다. 상수관망 설계에서 최적화된 설계를 제안하기 위해 다양한 최적화 알고리즘들이 적용되었다. 본 연구에서는 상수관망 최적설계에 자가적응형 매개변수를 개선한 Modified Hybrid Vision Correction Algorithm (MHVCA)을 적용하였다. 기존 Hybrid Vision Correction Algorithm (HVCA)의 Hybrid Rate (HR)를 비선형적 HR로 수정하여 성능을 개선하였다. 제안된 MHVCA의 성능을 확인하기 위해 결정변수가 2개 및 30개로 구성된 수학문제와 제약조건이 있는 수학문제에 적용하였다. MHVCA의 적용결과를 검토하기 위해 Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) 및 HVCA와 비교하였다. 최종적으로 MHVCA를 상수관망 최적설계 문제에 적용하여 결과를 다른 알고리즘들과 비교하였다. 수학문제 및 상수관망 설계 문제에서 MHVCA가 다른 알고리즘들에 비해 좋은 결과를 보여주었다. MHVCA는 본 연구에서 적용한 문제뿐만 아니라 다양한 수자원공학 문제에 적용하여 좋은 결과를 보여줄 수 있을 것이다.

The optimal design for water distribution system (WDS) is not only satisfying the minimum required water pressure of the nodes, but also minimizing pipe cost, etc. The number of designs of WDS increases exponentially due to the arrangement of various pipes. Various optimization algorithms were applied to propose an optimized design of WDS. In this study, Modified Hybrid Vision Correction Algorithm (MHVCA) with improved self-adapting parameter was applied to optimal design of WDS. The performance was improved by changing the Hybrid Rate (HR) of the existing Hybrid Vision Correction Algorithm (HVCA) to nonlinear HR. To verify the performance of the proposed MHVCA, it applied to mathematical problems consisting of 2 and 30 decision variables and constrained mathematical problems. In order to review the application results of MHVCA, it was compared with Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) and HVCA. Finally, MHVCA was applied to the optimal design problem of WDS and the results were compared with other algorithms. MHVCA showed better results than other algorithms in mathematical problems and WDS problem. MHVCA will be able to show good results by applying to various water resource engineering problems as well as problems applied in this study.

The impact of leakage was incorporated into the transfer functions of the complex head and discharge. The impedance transfer functions for the various leaking pipeline systems were also derived. Hydraulic transients could be efficiently analyzed by the developed method. The simulation of normalized pressure variation using the method of characteristics and the impulse response method shows good agreement to the condition of turbulent flow. The leak calibration could be performed by incorporation of the impulse response method with Genetic Algorithm (GA) and Harmony Search (HS). The objective functions for the leakage detection can be made using the pressure-head response at the valve, or the pressure-head or the flow response at a certain point of the pipeline located upstream from the valve. The proposed method is not constrained by the Courant number to control the numerical dissipation of the method of characteristics. The limitations associated with the discreteness of the pipeline system in the inverse transient analysis can be neglected in the proposed method.

The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.

An algorithm for helping the routing of heavy vehicles within an industrial setting is proposed. The system makes use of active RFID tags placed all over the industrial area. The algorithm aims to minimize shifts and loads of heavy vehicles in order to reduce production times and costs. Results are encouraging and the system is particularly adaptable to different scenarios. For example, it could be used by companies producing concrete to drive trucks to efficiently collect the materials needed for the product recipe.

The main objective of this study is to provide feature analysis of industrial accidents in manufacturing industries using CHAID algorithm. In this study, data on 10,536 accidents were analyed to create risk groups, Including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years (2002~2004) in Korea. The resulting classification rules have been incorporated into development of a developed database tool to help quantify associated risks and act as an early warning system to individual industrial accident in manufacturing industries.

The distance vector-hop wireless sensor node location method is one of typical range-free location methods. In distance vector-hop location method, if a wireless node A can directly communicate with wireless sensor network nodes B and C at its communication range, the hop count from wireless sensor nodes A to B is considered to be the same as that form wireless sensor nodes A to C. However, the real distance between wireless sensor nodes A and B may be dissimilar to that between wireless sensor nodes A and C. Therefore, there may be a discrepancy between the real distance and the estimated hop count distance, and this will affect wireless sensor node location error of distance vector-hop method. To overcome this problem, it proposes a wireless sensor network node location method by modifying the method of distance estimation in the distance vector-hop method. Firstly, we set three different communication powers for each node. Different hop counts correspond to different communication powers; and so this makes the corresponding relationship between the real distance and hop count more accurate, and also reduces the distance error between the real and estimated distance in wireless sensor network. Secondly, distance difference between the estimated distance between wireless sensor network anchor nodes and their corresponding real distance is computed. The average value of distance errors that is computed in the second step is used to modify the estimated distance from the wireless sensor network anchor node to the unknown sensor node. The improved node location method has smaller node location error than the distance vector-hop algorithm and other improved location methods, which is proved by simulations.

In clustering-based approaches, cluster heads closer to the sink are usually burdened with much more relay traffic and thus, tend to die early. To address this problem, distance-aware clustering approaches, such as energy-efficient unequal clustering (EEUC), that adjust the cluster size according to the distance between the sink and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes, because, in randomly distributed sensor networks, the approaches do not guarantee that the cluster energy consumption will be proportional to the cluster size. To address this problem, we propose a novel approach called CACD (Clustering Algorithm Considering node Distribution), which is not only distance-aware but also node density-aware approach. In CACD, clusters are allowed to have limited member nodes, which are determined by the distance between the sink and the cluster head. Simulation results show that CACD is 20%-50% more energy-efficient than previous work under various operational conditions considering the network lifetime.

The satisfiability problem is always a core problem in artificial intelligence (AI). And how to improve the efficiency of algorithms solving the satisfiability problem is widely concerned. Algorithm IER (Improved Extension Rule) is based on extension rule. The number of atoms and the number of clauses affect the efficiency of the algorithm IER. DPLL rules are helpful to reduce these numbers. Then a complete algorithm CIER based on splitting rule and extension rule is proposed in this paper in order to improve the efficiency. At first, the algorithm CIER (Complete Improved Extension Rule) reduces the scale of a clause set with DPLL rules. Then, the clause set is split into a group of small clause sets. In the end, the satisfiability of the clause set is got from these small clause sets'. A strategy MOAMD (maximum occurrences and maximum difference) for the algorithm CIER is given. With this strategy, a better arrangement of atoms could be got. This arrangement could make the number of small clause sets fewer and the scale of these sets smaller. So, the algorithm CIER will be more efficient.

Maintaining connectivity is essential in multi-hop wireless networks since the network topology cannot be pre-determined due to mobility and environmental effects. To maintain the connectivity, a critical point in the network topology should be identified where the critical point is the link or node that partitions the network when it fails. In this paper, we propose a new critical point identification algorithm and also present numerical results that compare the critical points of the network and H-hop sub-network illustrating how effectively sub-network information can detect the network-wide critical points. Then, we propose two localized topological control resilient schemes that can be applied to both global and local H-hop sub-network critical points to improve the network connectivity and the network resilience. Numerical studies to evaluate the proposed schemes under node and link failure network conditions show that our proposed resilient schemes increase the probability of the network being connected in variety of link and node failure conditions.

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

The common-source low noise amplifier(LNA) with inductive degeneration using a genetic algorithm is designed and tested for a down converter in an industrial, scientific and medical (ISM) band application and a wireless broadband internet service (WiBro). The genetic algorithm optimizes the reflection coefficients to be well matched the input and output ports between multistage transistor amplifiers, and it generates low voltage standing wave ratio as well as gain flatness of the amplifier. The stability and the gain flatness of the LNA have been improved by combining the matching circuits and the series feedback microstrip lines with inductive degeneration at common-source port. In the frequency range of ISM band and WiBro application operating at $2.3GHz{\sim}2.5GHz$, the measured power gain and maximum voltage standing wave ratio (VSWR) of the LNA are $41{\pm}0.5dB$ and 1.3, and the noise figure of the LNA is lower than 0.85dB. The above results are agreed well with the theoretical values of the amplifiers.

This study presents a systematic review of deep learning applications in dermatological diagnosis, analyzing 212 peer-reviewed studies published from 2019 to 2025. A structured search across major scientific databases identified methods, performance trends, and clinical relevance. Advanced architectures such as Vision Transformers, multimodal fusion models, and few-shot learning demonstrate substantial improvements in detecting both common and rare skin conditions. Despite these advances, challenges related to dataset imbalance, clinical integration, and fairness remain significant. The review outlines key research priorities to guide the development of equitable and clinically reliable AI-based dermatological solutions.

A reporting guideline can be defined as “a checklist, flow diagram, or structured text to guide authors in reporting a specific type of research, developed using explicit methodology.” A reporting guideline outlines the bare minimum of information that must be presented in a research report in order to provide a transparent and understandable explanation of what was done and what was discovered. Many reporting guidelines have been developed, and it has become important to select the most appropriate reporting guideline for a manuscript. Herein, I propose an algorithm for the selection of reporting guidelines. This algorithm was developed based on the research design classification system and the content presented for major reporting guidelines through the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. This algorithm asks 10 questions:“is it a protocol,” “is it secondary research,” “is it an in vivo animal study,” “is it qualitative research,” “is it economic evaluation research,” “is it a diagnostic accuracy study or prognostic research,” “is it quality improvement research,” “is it a non-comparative study,” “is it a comparative study between groups,” and “is it an experimental study?” According to the responses, 16 appropriate reporting guidelines are suggested. Using this algorithm will make it possible to select reporting guidelines rationally and transparently.

People nowadays use emoticons in their text increasingly in order to express their feelings or recapitulate their words. Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon. In this work, both modes of data were analyzed in combined and separately with both machine learning and deep learning algorithms for finding sentiments from twitter based airline data using several features such as TF?IDF, Bag of words, N-gram, and emoticon lexicons. This research demonstrates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual data analysis. Also, deep learning algorithms are found to be better than machine learning algorithms.

Adaptive beamforming is a fundamental technique in digital radar signal processing that suppresses interference while enhancing desired signal components. However, the conventional Least Mean Square (LMS) algorithm has inherent limitations in dynamic Multiple Input Multiple Output (MIMO) radar environments, which degrade the balance between convergence speed and steady state stability. To overcome these issues, we introduce a new method called MomentumLMD, which enhances the standard LMS by integrating Momentum and weight Decay. The Momentum component enhances the initial convergence speed, whereas the weight decay mechanism controls the excess kinetic energy to prevent overshoots and oscillations. The simulation results demonstrate that the proposed Least Mean Square with Momentum and Decay algorithm (LMD) outperforms the traditional LMS and standard Momentum + LMS, achieving the lowest Mean Square Error MSE in the steady state and maintaining robustness across a significantly wider hyperparameter range. Therefore, the LMD algorithm is established as the optimal choice for high precision, real-time Multiple Input Multiple Output (MIMO) radar applications.