• Advances in Computational Design
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• v.7 no.2
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• pp.153-171
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• 2022

The present study aimed to find a way to create forms that can simultaneously meet several architectural requirements by applying generative design methods specifically focused on cellular automata. In other words, it is tried to find various forms of architecture that all have common features. Because of the useful features of cellular automata, we decided to use it to generate various forms, but make a relation between the discrete nature of cellular automata and the continuous nature of architecture, was the major problem of our project. To achieve this goal, three consecutive stages were designed. In the first stage, independent variables including the location of the building, the height of the building, and the building area were considered as the inputs of the model. In the second stage, after locating the building, the building's main shell was designed as a hidden geometry for the cellular automata and then the cellular automata were determined based on this shell. The main result of this research is establishing a logical relationship between the discrete geometry of the cellular automata and the continuous search space such that it creates various optimized forms. Although we specify the site plan of this project at Iran-Tehran, this research can be generalized to various design sites as well as different projects, allowing the architectsto alter the cell dimensions, cell density, etc., based on their opinion and project needs.

• Smart Structures and Systems
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• v.29 no.3
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• Current Photovoltaic Research
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• v.10 no.2
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• pp.49-55
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• 2022

With rapid growth in light-harvesting efficiency from 3.8 to 25.8%, organic-inorganic hybrid perovskite solar cells (PSCs) have attracted great attention as promising photovoltaic devices. However, despite of their outstanding performance, the commercialization of PSCs has been suffered from severe stability issues, especially for UV and humidity: (i) UV irradiation towards PSCs is able to lead UV-induced decomposition of perovskite films or catalytic reactions of charge-transporting layers, and (ii) exposure to surrounding humidity causes irreversible hydration of perovskite layers by the penetration of water molecules, resulting considerable decrease in their power-conversion efficiency (PCE). This review investigates current status of strategies to enhance UV and humidity stability of PSCs in terms of UV-management and moisture protection, respectively. Furthermore, the multifunctional approach to increase long-term stability as well as performance is discussed as advanced research directions for the commercialization of PSCs.

• Tribology and Lubricants
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• v.37 no.6
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• pp.208-212
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• 2021

Chemical mechanical polishing (CMP) is a key technology used for the global planarization of thin films in semiconductor production and smoothing the surface of substrate materials. CMP is a type of hybrid process using a material removal mechanism that forms a chemically reacted layer on the surface of a material owing to chemical elements included in a slurry and mechanically removes the chemically reacted layer using abrasive particles. Sapphire is known as a material that requires considerable time to remove materials through CMP owing to its high hardness and chemical stability. This study introduces a technology using electrolytic ionization and ultraviolet (UV) light in sapphire CMP and compares it with the existing CMP method from the perspective of the material removal rate (MRR). The technology proposed in the study experimentally confirms that the MRR of sapphire CMP can be increased by approximately 29.9, which is judged as a result of the generation of hydroxyl radicals (·OH) in the slurry. In the future, studies from various perspectives, such as the material removal mechanism and surface chemical reaction analysis of CMP technology using electrolytic ionization and UV, are required, and a tribological approach is also required to understand the mechanical removal of chemically reacted layers.

• Korean Journal of Clinical Pharmacy
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• v.32 no.4
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• pp.281-287
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• 2022

Pharmacists should maintain professional competencies to provide optimal pharmaceutical care services to patients, which can be achieved through continued commitment to lifelong learning. Traditionally continuing education (CE) has been widely used as a way of lifelong learning for many healthcare professionals. It, however, has several limitations. CE is delivered in the form of instructor-led education focused on multiple learners. Learning is passive and reactive for participants, so it sometimes does not lead to bringing behavioral changes in workplace performance. Therefore, recently the concept of lifelong learning tends to move from CE toward continuing professional development (CPD). CPD is an ongoing process that improves knowledge, skills, and competencies throughout a professional's career. It is a more comprehensive structured approach toward the enhancement of personal competencies. It emphasizes an individual's learning needs and goals and enables learning to become proactive, conscious, and self-directed. CPD consists of four stages: reflect, plan, learn, and evaluate. CE is one component of CPD. Each stage is recorded in a CPD portfolio. There are many practical difficulties in implementing the complete CPD system for lifelong learning of pharmacists in many countries including Korea. Applying a hybrid form that utilizes CPD and CE together, as in the case of some countries, could be an alternative. Furthermore, in undergraduate pharmacy education, it is necessary to teach students about CPD and train them on how to perform CPD as a pharmacist.

• Transactions of Materials Processing
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• v.32 no.3
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• pp.122-128
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• 2023

As electric vehicles (EV) have increasingly replaced the conventional vehicles with internal combustion engines (ICE), most of automotive makers are actively devoting to the technology development of EV parts. Accordingly, the manufacturing process for power source has been also shifting from engine/transmission to EV motor/reducer system. However, lack of experience in developing the EV motor still remains as a technical challenge. In this paper, we employed the forming simulation based on finite element modeling to solve this problem. In particular, in order to increase the accuracy of the forming simulation, we introduced the elastic-plastic constitutive model parameters for polymer-copper hybrid wire by investigating the individual strain-stress curves, and elastic modulus of polymer and copper. Then, the reliability of modeling procedure was confirmed by comparing the simulated results with experiments. Finally, the identified mechanical properties and finite element modeling were applied to a hairpin forming process, which involves multiple deformation paths such as bending, pressing, widening, and twisting. The proposed numerical approach can replace common experience or experiment based trials by reducing production time and cost in the future.

• The Research Journal of the Costume Culture
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• v.31 no.4
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• pp.467-488
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• 2023

The purpose of this study is to determine the concepts of heterotopic image and fashion space, and the characteristics of fashion space and images from the perspective of fashion brands and users. This study examines the evolution of fashion space and consumers with it, based on Foucault's theory of heterotopia, which refers to spaces that blend contradictory features not typically found within a single physical structure. This is accomplished by employing a single case study of Ader Error's Ader Space, a Seoul-based brand known for its unique approach to presenting and communicating fashion. Based on an analysis of Instagram posts of Ader Error along with the hashtag searches "aderspace" and "adererror", this study categorizes heterotopia from the perspective of fashion brands into three properties: fashion space as a medium for selling fashion products; fashion space as getaway to hybrid fashion practices; and fashion space as an illusionary place to experience fashion. From the user perspective, the heterotopic image of Ader Space portrayed on Instagram is characterized by the image of fashion products in an extraordinary fashion space, the image of a fashion space beyond space and time, and the image of exposing the hidden and the illusion-compensation of fashion space. This study contributes to a heightened understanding of the evolutionary concept of the fashion space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

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

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.