A Knowledge-Based Approach of Automation Design of Aircraft Standard Components

2013 ◽  
Vol 431 ◽  
pp. 293-300
Author(s):  
Wei Zhang ◽  
Yan Min Li
Keyword(s):  
Aircraft Design ◽  
Solid Model ◽  
Clc Number ◽  
Document Code ◽  
Loosely Coupled ◽  
Knowledge Based ◽  
Novel Approach ◽  
Proposed Model ◽  
Standard Component ◽  
Standard Components

To solve the problem that knowledge of standard components cannot be quickly applied to design process of aircraft, a novel approach based on knowledge is proposed from the instantiated requirements of standard components. Knowledge-based model based on semantic of standard component is described, which is composed of parameters, rules, solid model and assembly information. The information model is expressed and stored based on XML. Knowledge-based model is provided to support the specifications determination, semantic modeling of solid model and batch assembly of standard components by loosely coupled manner. Design knowledge is seamlessly, quickly applied to the instantiated process of standard components. Effectiveness of the proposed model and method is verified by an aircraft design example of standard components. CLC number: TP391.7 Document code: A

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

scholarly journals Towards Recognition of Scale Effects in a Solid Model of Lattices with Tensegrity-Inspired Microstructure

Solids ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 50-59
Author(s):  
Wojciech Gilewski ◽  
Anna Al Sabouni-Zawadzka
Keyword(s):  
Mechanical Properties ◽  
Continuum Model ◽  
Scale Effects ◽  
Theory Of Elasticity ◽  
Solid Model ◽  
Gradient Theory ◽  
General Description ◽  
Normal Forces ◽  
Second Gradient ◽  
Proposed Model

This paper is dedicated to the extended solid (continuum) model of tensegrity structures or lattices. Tensegrity is defined as a pin-joined truss structure with an infinitesimal mechanism stabilized by a set of self-equilibrated normal forces. The proposed model is inspired by the continuum model that matches the first gradient theory of elasticity. The extension leads to the second- or higher-order gradient formulation. General description is supplemented with examples in 2D and 3D spaces. A detailed form of material coefficients related to the first and second deformation gradients is presented. Substitute mechanical properties of the lattice are dependent on the cable-to-strut stiffness ratio and self-stress. Scale effect as well as coupling of the first and second gradient terms are identified. The extended solid model can be used for the evaluation of unusual mechanical properties of tensegrity lattices.

Food waste treatment option selection through spherical fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Aysu Melis Buyuk ◽  
Gul T. Temur
Keyword(s):  
Treatment Option ◽  
Food Waste ◽  
Waste Treatment ◽  
Fuzzy Ahp ◽  
Treatment Options ◽  
Analytic Hierarchy ◽  
Food Treatment ◽  
Novel Approach ◽  
Proposed Model ◽  
Option Selection

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

scholarly journals Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows

2018 ◽  
Vol 7 (2.7) ◽  
pp. 203 ◽  
Author(s):  
Kalathiripi Rambabu ◽  
N Venkatram
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Application Layer ◽  
Continuous Growth ◽  
Loosely Coupled ◽  
Ddos Attack ◽  
Proposed Model ◽  
Flow Features ◽  
Target Network ◽  
Iot Devices

The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae” noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model

scholarly journals Automated Information Supply of Worker Guidance Systems in Smart Assembly Environment

2021 ◽  
pp. 235-248
Author(s):  
Gerhard Reisinger ◽  
Philipp Hold ◽  
Wilfried Sihn
Keyword(s):  
Core Competencies ◽  
Manufacturing Enterprises ◽  
3D Cad ◽  
Knowledge Based ◽  
Novel Approach ◽  
Complex Process ◽  
Cad Modelling ◽  
Smart Assembly ◽  
Guidance Systems ◽  
Scientific Challenge

AbstractThe global megatrends of digitization and individualization substantially affect manufacturing enterprises. Assembly workers are exposed to increased process complexity resulting in physical and cognitive workload. Worker guidance systems (WGS) are used to overcome this challenge through output of information regarding what should be done, how it should be done and why it should be done. An unsolved scientific challenge in this context is efficient information supply of WGS. Information such as worker’s instruction texts, pictures or 3D representations are created by employees of the work preparation department and transferred to the WGS. Manual information supply is a time-consuming and complex process, which requires a high (non-value-adding) effort as well as comprehensive knowledge in handling 3D CAD modelling and software programming. This paper presents a novel approach to reduce the required manual effort in information supply process. A knowledge-based model is proposed that enables an automated information supply of WGS in smart assembly environment by means of algorithms and self-learning expert systems, which pursues a holistic and consistent approach without media breaks. The automated approach assists employees of work preparation department, which means they can concentrate on their essential core competencies instead of being busy, for example, creating assembly plans, instruction texts or pictures for individual WGS. Finally, the technical implementation as a software-based proof-of-concept demonstrator and sub-sequent integration into the IT environment of TU Wien Pilot Factory Industry 4.0 is outlined.

scholarly journals A NOVEL APPROACH TO OPTIMIZE THE RIDESHARING PROBLEM USING GENETIC ALGORITHM

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 875-878
Author(s):  
S. Rangriz ◽  
M. Davoodi ◽  
J. Saberian
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Public Transportation ◽  
Noise Pollution ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Jam ◽  
Novel Approach ◽  
Proposed Model ◽  
Public Transportation Systems

Abstract. The enormous increase in the number of vehicles in the cities makes plenty of problems including air pollution, noise pollution, and traffic jam. Overcoming these annoying issues needs a significant plan in urban management such as using modern techniques in public transportation systems. Sharing either cars or taxies is one of the most interesting ways that has been used in some countries recently. In this phenomenon, 2 or 3 people use other’s car or taxi. In this article, an innovative approach to share taxies is proposed, and it uses a Genetic Algorithm to determine the placement of travelers in taxies. Therefore, some taxis will be switched off, and this helps to decrease urban traffic jam in cities. The results present that the proposed model turns off 69.8 % of taxies, and also 27.8 % of them carry more than one passenger; hence, this confirms the performance of the proposed model.

scholarly journals A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 5820-5825
Keyword(s):  
Physiological Signals ◽  
Classification Rate ◽  
Discrete Emotion ◽  
Novel Approach ◽  
Proposed Model ◽  
Depression Detection ◽  
Novel Method ◽  
Artificial Neural ◽  
Ann Classifier ◽  
Performance Rate

Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.

Automated monitoring and assessment of productivity in earthmoving projects

2018 ◽  
Vol 45 (11) ◽  
pp. 958-972 ◽  
Author(s):  
Ashraf Salem ◽  
Osama Moselhi
Keyword(s):  
Data Fusion ◽  
Continuous Monitoring ◽  
Research Work ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Cost Overruns ◽  
Knowledge Based ◽  
Monitoring Model ◽  
Multi Sensor Data Fusion ◽  
Proposed Model

Continuous monitoring of productivity and assessment of its variations are crucial processes that significantly contribute to success of earthmoving projects. Numerous factors may lead to productivity variations. However, these factors are subjectively identified using manual knowledge-based expert judgment. Such manual recognition process is not only subject to errors but also time-consuming. There is a lack of research work that focuses on near real-time assessment of productivity variation and its effect on cost, schedule and effective utilization of resources in earthmoving projects. This paper presents a customized multi-source automated data acquisition model that acquires data from a variety of wireless sensing technologies. The acquired multi-sensor data are transmitted to a central MySQL database. Then a newly developed data fusion algorithm is applied for truck state recognition, and hence the duration of each earthmoving state. Multi-sensor data fusion facilitates measurement of actual productivity, and consequently the assessment of productivity ratios that support continuous monitoring of productivity variation in earthmoving operations. The developed tracking and monitoring model generates an early warning that supports proactive decisions to avoid schedule delays, cost overruns, and inefficient depletion of resources. A case study is used to reveal the applicability of the proposed model in monitoring and assessing actual productivity and its deviations from planned productivity. Finally, results are discussed and conclusions are drawn highlighting the features of the proposed model.

How does open innovation affect firms’ innovative performance

2018 ◽  
Vol 12 (4) ◽  
pp. 720-740 ◽  
Author(s):  
Huiping Zhou ◽  
Yanhong Yao ◽  
Huanhuan Chen
Keyword(s):  
Open Innovation ◽  
Hierarchical Regression ◽  
Moderating Effects ◽  
Content Type ◽  
Innovative Performance ◽  
Knowledge Based ◽  
Opportunistic Behaviour ◽  
Proposed Model ◽  
New Finding ◽  
The Relationship

Purpose This paper aims to explore the direct effects of open innovation (OI) on firms’ innovative performance, and to examine the moderating effects of knowledge attributes, including knowledge distance, knowledge embeddedness and partner opportunism on this relationship. Design/methodology/approach Survey data of 247 samples from China were used to test the proposed model through hierarchical regression analysis. Findings The findings indicate that the dimensions of OI are positively related to innovative performance. The results also reveal that knowledge distance positively moderates the relationship between inbound OI and innovative performance, whereas knowledge embeddedness negatively affects that relationship. Knowledge embeddedness negatively affects the relationship between inbound OI and innovative performance, whereas knowledge distance positively moderates that relationship. Thus, a new finding is proposed that knowledge attributes could align effectively with specific OI type to achieve superior innovation outcomes. In addition, the empirical results suggest that partner opportunism plays a negative moderating role on the relationship between outbound OI and innovative performance. Originality/value The proposed view that a firm’s innovation outputs will be superior when its knowledge attributes effectively align with OI enriches studies of the OI context and expands the literature of both the resource-based view and the knowledge-based view. Furthermore, this study provides insights into how OI benefits can be influenced by external contexts from the perspective of partners’ opportunistic behaviour.

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