The primary purpose of this study is to develop system modules of school buildings and the seismic loss function of the system modules for regional loss assessment of school buildings. System modules of school buildings were developed through statistical analysis of school facilities in Korea. The structural system of school buildings with non-seismic details is defined as reinforced concrete with partially masonry walls (RCPM), and 27 system modules of RCPM were developed considering the number of stories, spans, and the age of the building. System modules were designed to assess the structural behavior by applying the shear spring model and the shear failure of the columns of the school building. Probabilistic seismic demand models for each component of system modules were derived through nonlinear dynamic analysis to determine the relationship between seismic intensity, drift ratio, and peak floor acceleration of system modules. The seismic loss function was defined as the total damage ratio, which is the ratio of replacement cost to repair cost to evaluate the seismic loss quantitatively. The system module-based seismic loss well predicted the observed data. It will be possible to help many stakeholders make risk-informed decisions for a region through the regional loss assessment of school buildings in Korea.

상수도 시스템에서의 사고 발생은 사용자들의 물 이용 불편으로 인해 막대한 사회경제적 피해를 초래할 수 있는 위협 요인이며, 따라서 수도사업자들은 수도정비기본계획 등을 통해 상수도 사고를 빠르게 복구하고, 피해 규모를 최소화하기 위한 다양한 노력을 기울이고 있다. 본 연구는 상수도 시스템에서 발생하는 관로사고 상황에 대하여 회복탄력성을 정량적으로 평가하고, 비상급수 방안을 포함한 사고 대응 전략의 효과를 분석하기 위한 평가 모형을 개발하였다. 개발 모형은 시스템의 회복탄력성에 기여하는 다양한 특성들을 반영할 수 있는 시간단위 공급 부족량과 충족률 지표를 통해 회복탄력성을 평가하며, 국내 지방상수도 시스템의 특정 구역을 대상으로 관로사고 시나리오를 모의하여 개발 모형의 적용 효과를 검증하였다. 결과적으로 개발 모형을 통해 비상연계관로, 배수지 충수, 병물 공급 등 비상대응 방안의 효과를 정량적으로 평가하였으며, 이를 통해 시스템의 회복탄력성 향상을 위한 설계 및 운영 전략 수립의 가능성을 확인하였다.

Today, as the social demand for tap water safety and the need for an ICT-based intelligent integrated control system increase, K-water (Korea Water Resources Corporation) is building and operating a ‘Water pipe monitoring CCTV system’ to quickly respond to crises in the event of a water leak. However, in the case of the existing system, when the CCTV rotates, the image information and the mapped water pipe image do net match, so the operator has the limitation that the water pipe image must be mapped anew every time. In this paper, in odert to solve the above problems, we propose an improved system that can extract feature points from CCTV images, detect changes in the coordinate values of the feature points, and automatically transform the location of the water pipe image by utilizing LoFTR (Detector-Free Local Feature Matching with Transformer), a type of deep learning image matching algorithm that is actively being studied in th field of the latest computer vision, and examine its effectiveness.

안정적이고 효율적인 수자원 공급을 보장하는 것은 가정, 산업, 공공 보건 분야 복지에 필수적이다. 상수도 시스템에서 이상을 감지하기 위해 데이터 모델, 수리 모델 기반 시뮬레이션 등 다양한 접근 방식을 통해 이상감지 역량이 꾸준히 향상되어 왔으나, 현장 적용 및 검증에 한계가 있어 실질적인 활용은 폭 넓게 이루어지지 못하고 있다. 실제 적용 가능한 이상감지 시스템 측면에서, 본 연구에서는 유량 및 압력 계측 데이터를 활용하여 시스템 내 이상 발생을 신속하게 감지하고 개략적인 위치를 파악하기 위한 실시간 이상감지 및 탐색 모델을 제안하였다. 제안된 모델은 유량수지 분석, 유량-수두손실 관계, EPANET 기반 수리 해석 방법을 통합하여 이상 감지 및 위치 파악의 정확성을 개선시키고자 하였다. 현장 실험 결과, 제안된 모델은 높은 신뢰도에서 시스템 내 이상유량의 발생을 효과적으로 감지하고, 발생 구간을 파악할 수 있는 것으로 나타났다. 본 연구 성과는 시스템의 실시간 이상 감지 및 운영관리를 위한 실효성 있는 접근 방식을 제공함으로써 상수도 시스템의 지속 가능하고 회복력 있는 운영관리에 기여할 것으로 기대된다.

Despite their historical use, studies on the genetic functions of mushrooms and varietal improvement via biomolecular techniques are limited compared to other organisms. Recent advancements in CRISPR/Cas9 have enabled precise genetic modifications in mushrooms, with RNP-based systems offering high editing efficiency without foreign gene insertion. In this study, we optimized gene-editing conditions for Ganoderma lucidum (Yongji 2) by utilizing RNP/nanoparticle complexes to enhance efficiency. The optimal conditions included a 0.2 M sorbitol buffer (pH 7.0) and a protoplast-to-complex ratio of 10:1. Among eight gRNAs designed for the catA gene, three were identified with high activity, and PEG-mediated transformation resulted in successful gene edits, primarily involving 1 bp deletions. The editing efficiency reached 7–8%, demonstrating that nanoparticle-supported RNP systems are effective for marker-free gene editing in mushrooms. These findings highlight a promising approach for advancing genetic research and varietal improvement in G. lucidum and other mushroom species.

With technological and social development, high-rise atypical buildings have emerged. In order to take into account the structural vulnerability due to their high-rise atypical shape, systems such as vibration control system and seismic isolation can be applied. In this study, dynamic behavior characteristics analysis was conducted based on the location of the seismic isolation system installation of the atypical facade shape Tapered and reverse shell structure models. With the installation of Lead Rubber Bearing(LRB), the maximum story drift ratio showed a decrease, but the maximum absolute acceleration showed a phenomenon in which the response was amplified in the middle and low story. LRB1(base isolation system) is the most effective for simultaneous control of the two dynamic responses, but the 46th floor of ‘Nor’ and’ RS’ and the 41st floor of ‘TA’ are considered the most effective installation location of the seismic isolation system in consideration of the burden of the seismic isolation system and the structure stability.

This study aims to implement an integrated control system for a micro drill bit grinding machine to increase the processing stability and production efficiency of the equipment. The system consists of a WTGM mechanism, an environmental measurement sensor (RMU device), a control server, and a control client, and collects production statistics and alarm information in real time to enable central monitoring and statistical analysis. Through the control system, managers can check data and solve problems anytime and anywhere, thereby increasing the stability and efficiency of the production process. As a result of the experiment, it showed excellent performance in all evaluation items such as alarm occurrence time, notification time, and event operation time through temperature and humidity sensors, and contributed to productivity improvement through immediate response through e-mail and SNS notification. In conclusion, the implemented system optimizes the operating rate and inventory management of the equipment through real-time monitoring and yield analysis, and it is expected to improve system performance as it can be used as learning material for pattern analysis and deep learning algorithms in the future.

The purpose of this study is to experimentally analyze the seismic performance of a vertical irregular beam-column specimen reinforced with RBS (Replaceable Steel Brace System), a steel brace system. To evaluate the seismic performance of RBS, three specimens were manufactured and subjected to cycle loading tests. The stiffness ratio of beam-upper column of the non-retrofitted specimen was 1.2, and those of the two retrofitted specimens were 1.2 and 0.84. The stiffness ratio of the beam-lower column of all specimens was 0.36. And the stiffness ratio were used for variable. As a result of the experiment, the specimen retrofitted with RBS showed improved maximum load, effective stiffness and energy dissipation capacity compared to the non-retrofitted specimen with the same beam-upper column stiffness ratio. The specimen with 0.84 beam-upper column stiffness ratio showed improved performance compared to the specimen with 1.2 stiffness ratio.

The diagrid structural system has a braced frame that simultaneously resists lateral and vertical loads, and is being applied to many atypical high-rise buildings for aesthetic effects. In this study, a 60-story structure with twisted degrees of 0° to 180° was selected to determine seismic response control performance of twisted high-rise structures whether the diagrid system was applied and according to the reduction of braced frame material quantity. For this purpose, ‘Nor’ model without the diagrid system and the ‘DS’ model with the diagrid system, which was modeled by reducing braced frame member section to 700~400, were modeled. As a result, the 'DS' model showed an seismic response control effect in all Twisted models even when the quantity was reduced, and especially, the Twisted shape model was found to have an superior response control effect compared to the regular structure. In addition, the ‘600DS’ analysis model, which matched the ‘Nor’ model by 99.0% in quantity, showed an increase in seismic response control performance as the rotation angle increased.

This study aimed to develop a basic bodice suitable for the body shape of women in their 50s by using the CLO 3D virtual clothing program to create an avatar with the standard body presented in KATS (2022). A pattern was designed and produced using the CLO program, and the virtual fitting was evaluated. First, it was possible to confirm the slope (profile line) from the front center line to the point in front of the neck, the sagging shape of the breast, and the change in body shape due to the increase in bust circumference using the CLO program. Second, the change in body surface and body surface length according to movement was used to identify the profile line. The bust dart was very large. and the bust circumference showed the largest changes (5.05% in the 90° side and 1.5% of the breast width). Third, the total length reference line of the basic bodice was the back length (actual measurement). The total width reference line was the bust circumference/4+2cm and the breast width+0.5cm. The armhole depth reference line was the bust circumference/4–2. A profile line was created from the front neck circumference to the neck shoulder point to the breast point. Darts were formed on the back shoulder and back armhole lines. The allowances were 8 and 6cm for the bust and waist circumferences, respectively. All virtual fitting evaluation items improved significantly over two rounds (from 3.69/5 to 4.66/5 points).

This study aims to identify crisis signs in small and medium enterprise (SME)-concentrated regions and establish measures to prevent economic recession and normalize regional economies through proactive responses. To achieve this, we investigated and analyzed the crisis status and outlook of companies located in Jeonbuk, their detailed management conditions, management issues by industry, difficulties in business operations, and policy demands. Out of 4,144 SMEs in Jeonbuk's concentrated areas, 270 companies responded to the survey. The results showed that 60% of the responding companies perceived their current management situation as being in a state of crisis. However, the outlook for the next quarter and the following year is expected to improve. Notably, compared to manufacturing companies, non-manufacturing firms responded that their crisis situation in the next quarter would not improve and expected the crisis to persist. In terms of detailed business conditions, regardless of the distinction between manufacturing and non-manufacturing sectors, all aspects of the survey, including domestic sales, export sales, operating profit, financial status, and the number of employees, indicated better prospects for the next quarter and the following year compared to the current quarter. The study's findings suggest that companies in SME-concentrated areas of Jeonbuk are relatively accurate in recognizing the crisis situation of their own businesses and operating markets. Additionally, the companies responded that crisis monitoring is necessary. Differences in difficulties faced by the manufacturing and non-manufacturing sectors imply the need for industry-specific financial support programs. Based on the survey results, we propose financial support projects tailored to the manufacturing and non-manufacturing sectors, considering the degree of market competition. For more precise research, future studies will involve extracting larger samples and conducting a detailed analysis by subdividing manufacturing sectors (e.g., food, metal) and non-manufacturing sectors (e.g., agriculture, design).

Sequential zone picking is an order picking method designed to enhance warehouse efficiency by dividing the storage area into multiple zones and picking items in a sequential order across these zones. Picked items are often placed in dedicated totes and transported between zones using a conveyor system, which manages the picking flow but can occasionally result in inefficiencies during the process. This study presents a variant of the sequential zone picking system, called a dual-lane zone picking system (DZP), which consists of two parallel conveyor lanes without buffers between consecutive zones. This conveyor configuration allows the picker in each zone to alternate processing between the two lanes, thereby lessening the constraints of tote transitions between zones and improving both system throughput and picker utilization. We design and conduct a series of experiments using a discrete-event simulation model to evaluate the performance of DZPs. The experiment results indicate that DZP surpasses the original single-lane zone picking system by shortening the system’s mean flow time in low flow intensity scenarios and achieving a higher maximum throughput and worker utilization in high flow intensity scenarios. Additionally, we investigate the effects of the number of zones and order batching size on the performance of DZP to gain further insights into the system’s operational control.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

This study explores the utilization level of smart manufacturing systems in the value chain processes of manufacturing and empirically examines the effect of the utilization level of these systems on manufacturing competitiveness in SMEs. Smart manufacturing systems in the value chain processes are categorized into Sales, Purchasing, Production & Logistics, and Support systems. By analyzing the research model using structural equation modeling, this study identifies that Sales systems, Purchasing systems, Production & Logistics systems, and Support systems have a significant impact on manufacturing process efficiency. Additionally, Production & Logistics systems and manufacturing process efficiency positively and significantly influence manufacturing competitiveness. The findings suggest that the utilization of information is directly and positively related to manufacturing process efficiency, including reducing lead-time, decreasing work performance man-hours (M/H), and improving work accuracy. These improvements ultimately have a significant impact on manufacturing competitiveness. In conclusion, the use of smart manufacturing systems is becoming an integral part of the manufacturing industry. To gain a competitive edge, it will be necessary to introduce and utilize optimal smart manufacturing systems, taking into account the size of manufacturing firms.

Recently, in the manufacturing industry, changes in various environmental conditions and constraints appear rapidly. At this time, a dispatching system that allocates work to resources at an appropriate time plays an important role in improving the speed or quality of production. In general, a rule-based static dispatching method has been widely used. However, this static approach to a dynamic production environment with uncertainty leads to several challenges, including decreased productivity, delayed delivery, and lower operating rates, etc. Therefore, a dynamic dispatching method is needed to address these challenges. This study aims to develop a reinforcement learning-based dynamic dispatching system, in which dispatching agents learn optimal dispatching rules for given environmental states. The state space represents various information such as WIP(work-in-process) and inventory levels, order status, machine status, and process status. A dispatching agent selects an optimal dispatching rule that considers multiple objectives of minimizing total tardiness and minimizing the number of setups at the same time. In particular, this study targets a multi-area manufacturing system consisting of a flow-shop area and a cellular-shop area. Thus, in addition to the dispatching agent that manages inputs to the flow-shop, a dispatching agent that manages transfers from the flow-shop to the cellular-shop is also developed. These two agents interact closely with each other. In this study, an agent-based dispatching system is developed and the performance is verified by comparing the system proposed in this study with the existing static dispatching method.

본 연구는 케이트 크로포드와 블라단 욜러의 협업 프로젝트인 <AI 시스템의 해부>를 중심으로 인간, 사회, 지구를 관통하는 인공지능 시스템의 작동에 대한 그들의 비판적 담론을 읽어내는 데에 목적이 있다. <AI 시스템의 해부>는 인공지능 음성인식 스피커인 아마존 에코를 사례로 삼아 인공지능 기술세계의 물질적, 사회적 조건을 가시화한 ‘데이터 시각화’로서, 인공지능 시스템의 이면에 감추어진 노동, 데이터, 자원의 무자비한 추출 구조를 드러낸 해부학적 지도이다. 크로포드와 욜러는 인공지능 기술이 작동하는 기술세계의 지형을 탐구하고 시각화하 기 위해 ‘비판적 지도제작’을 중요한 인식적, 실천적 방법으로 사용해 왔다. 이에 본고는 <AI 시스템의 해부>에 대한 ‘지도 읽기’를 수행하되, 철학자 레비 브라이언트가 ‘존재지도학’에서 제시하는 지형학의 네 가지 요소를 범주로 삼아 거대 기술세계 지형도의 구조와 의미를 분석하였 다. 이를 통해 본고는 크로포트와 욜러의 지형도가 인공지능 기술세계의 광범위한 추출주의를 비판적으로 가시화하고 있음을 강조하였다.

본 연구는 대서양연어 (Salmo salar) 파르를 대상으로 다른 광주기 (L24:D0, L15:D9, L12:D12, L9:D15, L0:D24)에 60일간 노출시킨 후에 생존, 성장 및 혈액성분에 관한 영향을 연구하였다. 실험종료 시 생존율의 측정결과 L24:D0 실험구는 90.0± 7.1%, L12:D12 실험구는 87.5±3.5%, L9:D15 실험구는 97.5±3.5%, L24:D0 실험구는 97.5±3.5%로 나타났으나 각 실험구 간의 유의한 차이는 없었다. 실험종료 시 각 실험구의 증체율 (weight gain, WG), 일간성장률 (specific growth rate, SGR) 및 사료효율 (feed efficiency, FE)의 변화를 측정한 결과, 광주기 차이에 따른 유의한 변화는 보이지 않았다. 혈장 성분 중 ALT (alanine aminotransferase), AST(Aspartate Aminotransferase) 및 glucose는 L24:D0 실험구가 다른 실험구에 비해 유의하게 상승한 것으로 나타났다. 혈장 cortisol은 L24:D0와 L0:D24가 가장 높았으며, L15:D9와 L9:D15와는 유의한 차이는 보이지 않았지만, L12:D12보다는 유의하게 높은 것으로 나타났다. 혈장 sodium (Na+), potassium (K+), chloride (Cl-) 및 osmolality는 각 실험구 간에 유의한 차이는 보이지 않았다. 본 연구결과, 대서양연어 파트에 대해서 60일간 다른 광주기에 노출 시켰을 때 생존 및 성장도의 변화는 보이지 않았으나, L24:D0 실험구에서 조직손상과 스트레스 지표인 혈장 ALT, AST, cortisol 및 glucose 농도가 유의하게 상승하는 것으로 나타났다.

The precast concrete (PC) method allows for simple assembly and disassembly of structures; however, ensuring airtight connections is crucial to prevent energy loss and maintain optimal building performance. This study focuses on the analytical investigation of the shear capacity of precast ultra-high-performance concrete (UHPC) ribs combined with standard concrete PC cladding walls. Five specimens were tested under static loading conditions to evaluate their structural performance and the thermal behavior of the UHPC rib shear keys. Test results indicated that the specimens exhibited remarkable structural performance, with shear capacity approximately three times greater than that of standard concrete. Numerical models were subsequently developed to predict the shear capacity of the shear keys under various loading conditions. A comparison between the experimental results and finite element (FE) models showed a maximum strength difference of less than 10% and a rib displacement error of up to 1.76 mm. These findings demonstrated the efficiency of the FE model for the simulation of the behavior of structures.

Purpose: To highlight the experiences of nurses who are promoted through a career development system that recognizes and rewards their expertise based on their clinical experience. Methods: This study involved 11 nurses who voluntarily participated at Clinical Nurse III level or above from a tertiary hospital. Data were collected through individual in-depth interviews from March 4 to 28, 2024, using semi-structured and open-ended questions. Analysis was performed using the Colaizzi' phenomenological method. Results: The results of the study were derived into 32 themes, 13 theme clusters, and 5 categories: ‘The promotion process you chose to grow as a leader’, ‘A rugged climb to professional recognition’, ‘Glorious CN III title’, ‘Reborn as a professional nurse’, and ‘The career ladder system needs improvement but it is essential’. Conclusion: This study revealed the experiences of CN III nurses who were promoted through the career development system. Nurses hope that this system will allow them to articulate their expertise more clearly and be rewarded for their valuable experiences. By assessing the career development needs of the nurses, we aimed to better recognize their skills and enhance the significance of their experiences.