Manual Designs Research Articles

Zygomatic Osteotomy surgery design software based on skull CT scans - Self-supervised algo reduces workload.

The morphology of the zygomatic complex significantly influences facial appearance, leading to a focus on zygomatic osteotomy. The current technique, the "L-shaped" zygomatic osteotomy, requires a small incision and preoperative osteotomy design for an osteotomy guide. However, the use of multiple software programs in the design process makes it time-consuming and clinically challenging. Artificial intelligence technology offers a solution by integrating digital medical technology into medicine. AI algorithms were developed based on point cloud models, using 2000 cases of three-dimensional CT data for training. Eighty CT data sets were randomly chosen for both AI and manual skull segmentation designs. The effectiveness, symmetry, safety, and aesthetic outcomes were compared. The AI zygomatic osteotomy showed superior performance in symmetry and aesthetics compared to manual zygomatic osteotomy. The complex structure of the zygomatic arch highlights the advantages of AI-driven osteotomy design, especially in intricate cases. Additionally, the AI osteotomy scheme demonstrated no compromise in safety indicators compared to the manual approach. AI zygomatic osteotomy proves to be a safe and effective alternative to manual zygomatic osteotomy, showcasing enhanced symmetry and aesthetic outcomes. The efficiency and precision of AI-driven design in complex zygomatic osteotomies make it a promising advancement in this field.

Design of Geothermal Groundwater Heating and Cooling Plant: A Trial Study in Eleme Fertilizer Company in Niger Delta Region

A geothermal groundwater heating and cooling plant consisting of a heat pump and heat exchanger has been designed. Geothermal heating and cooling systems play a crucial role in the decarbonization of the environment, helping to prevent global warming and promote energy savings due to the reduced energy required to power the plant, which in turn generates more geothermal energy for the heating and cooling needs of the fertilizer company. This study considered Eleme Fertilizer as a case study for the scaled-up and installation of the prototype designed of the geothermal groundwater heating and cooling plant. Both manual and software simulation designs were conducted based on material and energy balance principles. Error analysis and deviations were used to validate the design results. The results of the plant's unit design showed that the power drive, overall efficiency, and coefficient of performance for heating and cooling were 354.1 kW, 402%, and 2.82 & 1.05, respectively. For the heat exchanger, design type 10-E-01, the values for heat duty, overall heat transfer coefficient, exchanger area, and tube pressure drop were 309.5 kW, 0.2104 kW/m²°C, 60.32 m², and 390 MPa, respectively. Error analysis conducted on the design work showed negligible values of RMSE (0.02, 0.025, 20 and 0.656) and deviation (0.014, 0.05, 0.05, 0.002) for coefficient of performance of heating and cooling, heat pump efficiency and the drive power of the plant respectively. The values obtained from the design of the units in the geothermal plant were reasonable and are thus recommended for academic and industrial applications

Adaptive evolutionary neural architecture search based on one-dimensional convolutional neural network for electric rudder fault diagnosis

Abstract The electric rudder is the core actuator of the flight control system. Fault diagnosis of rudders is essential for the production and repair of rudders. While existing methods for rudder fault diagnosis are effective, the manual design of neural network models is a time-consuming and challenging process. Therefore, this paper proposes a fault diagnosis framework for the electric rudder based on an adaptive evolutionary neural architecture search (AENAS-FD). AENAS-FD employs an adaptive strategy to guide the evolution of a one-dimensional convolutional neural network towards achieving optimal diagnostic accuracy. This adaptive strategy adjusts the relevant parameters of the genetic operator based on the relationship between individual and population fitness. This leads to improved algorithm search performance and mitigates premature convergence. The experiments on the real electric rudder dataset demonstrate that AENAS-FD can generate superior network architectures for diagnosing rudder faults, exhibiting better diagnostic accuracy when compared to manually designed networks.

Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

Designing an adaptive learning framework for predicting drug-target affinity using reinforcement learning and graph neural networks

Design of 1.1T/300mm Bitter-type magnet for gyro-traveling-wave tube with power consumption optimization theory.

To improve the portability of magnets in gyrotron devices, we designed a compact Bitter-type magnet with power consumption optimization theory. This magnet operates at room temperature in a small volume. The theory revises existing electromagnetic theory for non-uniform structural Bitter-type magnets and achieves the lowest energy consumption through iterative optimization. To extend the magnetic field homogeneity region, the ferromagnetic material armature is applied to the Bitter-type system without additional power consumption. Unlike previous manual designs, the proposed Bitter-type magnets can obtain optimal parameters with a significant reduction in computing time. Through the introduction of correction factors, we improve accuracy through multiple verifications of simulations and experiments. On this basis, a room-temperature Bitter-type magnet system for Ka-band fundamental mode gyrotron amplifiers is designed. Its maximum magnetic field strength is 1.1T, and the length of the homogeneity region is 300mm. Through optimization, its energy consumption is only 27.5 kW.

FDE-Net: A memory-efficiency densely connected network inspired from fractional-order differential equations for single image super-resolution

FDE-Net: A memory-efficiency densely connected network inspired from fractional-order differential equations for single image super-resolution

Assessing embodied carbon of flat slab buildings – An ANN-integrated optimization methodology

Assessing embodied carbon of flat slab buildings – An ANN-integrated optimization methodology

Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-Free Multi-Exposure Image Fusion

Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. Despite its advancements, the field grapples with challenges, notably the reliance on manual designs for network structures and loss functions, and the constraints of utilizing simulated reference images as ground truths. Consequently, current methodologies often suffer from color distortions and exposure artifacts, further complicating the quest for authentic image representation. In addressing these challenges, this paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions. More specifically, we harness a unique dual research mechanism rooted in a novel weighted structure refinement architecture search. Besides, a hybrid supervised contrast constraint seamlessly guides and integrates with searching process, facilitating a more adaptive and comprehensive search for optimal loss functions. We realize the state-of-the-art performance in comparison to various competitive schemes, yielding a 10.61% and 4.38% improvement in Visual Information Fidelity (VIF) for general and no-reference scenarios, respectively, while providing results with high contrast, rich details and colors. The code is available at https://github.com/RollingPlain/HSDS_MEF.

Few-Shot Font Generation With Weakly Supervised Localized Representations.

Automatic few-shot font generation aims to solve a well-defined, real-world problem because manual font designs are expensive and sensitive to the expertise of designers. Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style. However, this approach limits the model in representing diverse local styles because it is unsuitable for complicated letter systems. For example, Chinese characters consist of a varying number of components (often called "radical") with a highly complex structure. In this paper, we propose a novel font generation method that learns localized styles, namely component-wise style representations, instead of universal styles. The proposed style representations enable synthesizing complex local details in text designs. However, learning component-wise styles solely from a few reference glyphs is infeasible when a target script has a large number of components, for example, over 200 for Chinese. To reduce the number of required reference glyphs, we represent component-wise styles by a product of component and style factors inspired by low-rank matrix factorization. Owing to the combination of strong representation and a compact factorization strategy, our method shows remarkably better few-shot font generation results (with only eight reference glyphs) than other state-of-the-art methods. Moreover, strong locality supervision was not utilized, such as the location of each component, skeleton, or strokes. The source code is available at https://github.com/clovaai/lffont.

Evolving many-objective dispatching rule pairs for unrelated parallel machine scheduling with sequence-dependent setup times

The real-world problem of unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSPSST) is investigated and focuses on two key aspects: fast solution and many-objective optimization. While dispatching rules (DRs) are effective in meeting demand, the manual design of many-objective DRs (MaODRs) is challenging. An alternative approach, based on the automatic evolution of MaODRs using hyper-heuristics, shows promise. However, limited literature exists on the automatic evolution of MaODRs specifically for UPMSPSST. In this study, a many-objective genetic programming (MaOGP) algorithm is formed by combining many-objective evolutionary algorithms (MaOEAs) with genetic programming (GP) algorithms. This enables the automatic evolution of MaODRs, leveraging the respective advantages of each approach. The exploration results demonstrate that the evolved MaODRs outperform manually designed DRs reported in prior literature. Furthermore, when considering combinations of objectives with different correlations, the MaODRs exhibit outstanding performance across multiple objectives simultaneously, surpassing even the performance of DRs trained separately for individual objectives. These findings highlight the potential of the proposed MaOGP approach in efficiently and effectively solving the UPMSPSST problem, opening avenues for further research and practical applications in related industries.

LRNAS: Differentiable Searching for Adversarially Robust Lightweight Neural Architecture.

The adversarial robustness is critical to deep neural networks (DNNs) in deployment. However, the improvement of adversarial robustness often requires compromising with the network size. Existing approaches to addressing this problem mainly focus on the combination of model compression and adversarial training. However, their performance heavily relies on neural architectures, which are typically manual designs with extensive expertise. In this article, we propose a lightweight and robust neural architecture search (LRNAS) method to automatically search for adversarially robust lightweight neural architectures. Specifically, we propose a novel search strategy to quantify contributions of the components in the search space, based on which the beneficial components can be determined. In addition, we further propose an architecture selection method based on a greedy strategy, which can keep the model size while deriving sufficient beneficial components. Owing to these designs in LRNAS, the lightness, the natural accuracy, and the adversarial robustness can be collectively guaranteed to the searched architectures. We conduct extensive experiments on various benchmark datasets against the state of the arts. The experimental results demonstrate that the proposed LRNAS method is superior at finding lightweight neural architectures that are both accurate and adversarially robust under popular adversarial attacks. Moreover, ablation studies are also performed, which reveals the validity of the individual components designed in LRNAS and the component effects in positively deciding the overall performance.

Supporting early-stage design decisions with building performance optimisation: Findings from a design experiment

Supporting early-stage design decisions with building performance optimisation: Findings from a design experiment

Automated tooth crown design with optimized shape and biomechanics properties

Despite the large demand for dental restoration each year, the design of crown restorations is mainly performed via manual software operation, which is tedious and subjective. Moreover, the current design process lacks biomechanics optimization, leading to localized stress concentration and reduced working life. To tackle these challenges, we develop a fully automated algorithm for crown restoration based on deformable model fitting and biomechanical optimization. From a library of dental oral scans, a conditional shape model (CSM) is constructed to represent the inter-teeth shape correlation. By matching the CSM to the patient’s oral scan, the optimal crown shape is estimated to coincide with the surrounding teeth. Next, the crown is seamlessly integrated into the finish line of preparation via a surface warping step. Finally, porous internal supporting structures of the crown are generated to avoid excessive localized stresses. This algorithm is validated on clinical oral scan data and achieved less than 2 mm mean surface distance as compared to the manual designs of experienced human operators. The mechanical simulation was conducted to prove that the internal supporting structures lead to uniform stress distribution all over the model.

A quantum particle swarm optimization-based optimal LQR-PID controller for load frequency control of an isolated power system

One of the characteristics of a robust power grid is minimal variations in its frequency to load change or loss in generating unit(s). From the perspective of optimal control theory, the issue of load frequency control in the context of the interconnected functioning of power systems is investigated in this work, and a novel load frequency controller is proposed for a single area isolated power network. This novelty incorporates all the primary characteristics of the solutions that are based on a mixture of optimal controller designs by establishing a linear quadratic regulator optimized with quantum particle swarm optimization to design a proportional integral derivative (PID) controller unlike the conventional PID controller designs that are based on a combined Ziegler-Nichols and root locus (ZN-RL) method and manual tuning. The simulation results of the proposed controller using MATLAB show its efficacy in not only ensuring that there is no steady-state error in terms of the system frequency with load changes but also in achieving smoother transients. Following these landmark achievements, a transfer function model of the resulting power grid is constructed. The outcome of the model reveals that the system transients have been improved while keeping the intended steady-state characteristics. Furthermore, it is observed that the proposed load frequency controller has the best performance when compared with the combined ZN-RL method and the manual PID designs. This, therefore, demonstrates the superiority of the proposed design for load frequency control in power systems.

Pelatihan Pembuatan Desain Busana Secara Digital Bagi Peserta Didik SMK Tata Busana di Yogyakarta

This training activity aims to provide knowledge and skills in making digital fashion designs as a continuation of the process of making manual fashion designs for students of SMK Fashion Yogyakarta. The target of the activity is the third-year students of SMK (vocational high school) of the Fashion Expertise Program in Yogyakarta. The lecturers doing off-campus project activities related to fashion design provide training on making fashion designs digitally with the CLO 3D application. In the form of training, the speaker not only delivers material or theory but also conducts training directly to students. This activity is divided into three stages: the preparation stage, the implementation stage, and the evaluation stage. The methods used in this training activity are lectures to convey fashion design theory, demonstration methods to provide examples of the process of making fashion designs digitally, and the practice of making fashion designs using the CLO 3D application carried out by students accompanied by a presenters team. The activity results are (1) Implementing practical activities to make fashion designs with digital techniques attended by three classes, XII Busana 1, 2, and 3, consisting of 90 students. (2) Students are trained in making fashion designs with digital techniques using the CLO 3D application. Based on the evaluation of digitally making fashion designs, participants' attendance during the training reached 96%. While based on student practice results, 62% of student design result is categorized as 'good,' while 38% means fairly good.

Neural Architecture Search with In‐Memory Multiply–Accumulate and In‐Memory Rank Based on Coating Layer Optimized C‐Doped Ge2Sb2Te5 Phase Change Memory

AbstractNeural architecture search (NAS), as a subfield of automated machine learning, can design neural network models with better performance than manual design. However, the energy and time consumptions of conventional software‐based NAS are huge, hindering its development and applications. Herein, 4 Mb phase change memory (PCM) chips are first fabricated that enable two key in‐memory computing operations—in‐memory multiply‐accumulate (MAC) and in‐memory rank for efficient NAS. The impacts of the coating layer material are systematically analyzed for the blade‐type heating electrode on the device uniformity and in turn NAS performance. The random weights in the searched network architecture can be fine‐tuned in the last stage. With 512 × 512 arrays based on 40 nm CMOS process, the PCM‐based NAS has achieved 25–53× smaller model size and better performance than manually designed networks and improved the energy and time efficiency by 4779× and 123×, respectively, compared with NAS running on graphic processing unit (GPU). This work can expand the hardware accelerated in‐memory operators, and significantly extend the applications of in‐memory computing enabled by nonvolatile memory in advanced machine learning tasks.

A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry

A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry

Light weight concrete mix design through fuzzy logic with Cosine similarity aided optimized rules

Light weight concrete mix design through fuzzy logic with Cosine similarity aided optimized rules

Dapper: Deploying Service Function Chains in the Programmable Data Plane Via Deep Reinforcement Learning

Network functions perform specific packet processing on network traffic. To meet operators' needs, forming service function chains (SFCs) is a fundamental technique used in today's ISPs and datacenter networks. Implementing SFCs in the programmable data plane with high throughput and low latency is a new approach to satisfy demands of ever-growing network traffic. Previous works have proposed different solutions to solve the problem, but they all inevitably have to make trade-offs between running time and performance. For example, an ILP (Integer Linear Programming) can optimize cost but suffers from long running time in large-scale network topologies. Heuristic algorithms depend strongly on manual designs and usually have a performance gap with the optimal solution. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> , a framework for deploying SFCs in the programmable data plane using DRL (Deep Reinforcement Learning) with graph convolutional network. In order to expand the searching space to prevent the optimal value from being missed, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> allows the RL (Reinforcement Learning) agent to simultaneously extract features from both the substrate network and the hardware pipeline, and exploit a graph convolutional network to enhance performance. Moreover, a mask mechanism is also designed to accelerate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> and improve its scalability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> has been implemented and extensively evaluated on both P4 hardware switches (equipped with Intel Tofino ASIC) and software switches (i.e., bmv2). Experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> can automatically generate deployment solutions in a few seconds of running time after training. They also demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dapper</i> reduces hardware stage usage and the latency of SFCs by up to 17.8% and 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 73% respectively on average when compared with heuristics.

Stochastic analysis of dig limit optimization using simulated annealing

The results of dig limit delineation in open pit mining are never truly optimized due to gaps in the underlying data, such as insufficient sampling. Aside from the data uncertainty, there is also an influence on the final dig limit by either humans or by the heuristic character of an optimization method like simulated annealing. Several dig limit optimizers have been published, which can replace the manual dig-limits designing process. However, these dig limit designs are generally not adapted to account for this heuristic character. In this paper we present a stochastic analysis tool that can be used with the results of heuristic dig-limit optimization to increase confidence in the obtained results. First, an enhanced simulated annealing algorithm for dig limit optimization is presented. Then, this algorithm is tested on ten different blasts at the Marigold mine, Nevada, USA, as a case study. Finally, the results are analysed with a destination-based ensemble probability map and an analysis conducted of the final solution data distribution. The generated dig-limit designs of the algorithm include high revenue areas that are excluded in comparable manual designs and show improved objective and revenue values. The analysis tool provides block destination probabilities and box plots with the distribution of opportunity value for the dig limit. Furthermore, with the analysis tool, it is possible to make well-informed design decisions in areas of uncertainty.