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نتیجه جستجو - Integrated Approach

تعداد مقالات یافته شده: 36
ردیف عنوان نوع
1 Analysing the inhibitors of complexity for achieving sustainability and improving sustainable performance of petroleum supply chain
تجزیه و تحلیل مهارکننده های پیچیدگی برای دستیابی به پایداری و بهبود عملکرد پایدار زنجیره تأمین نفت-2021
In the era of business sustainability, the modern supply chain is becoming complex due to several inhibitors such as uncertainty in the market, technological innovation, environmental protocols, cross-border trade regulations, and many stakeholders’ involvement. In the existing literature, minimal discussion to study the inhibit supply chain complexity (SCC) inhibitors for achieving sustainability. Therefore, the study analyses the inhibitors to SCC and supply chain sustainability (SCS) jointly. The combined examination of the underlying relationship for improving the Petroleum Supply Chain’s sustainable performance (PSC) is arguably one of the complex sectors with a significant impact on the environment and sustainability. The inhibitors to SCC and SCS are identified through extensive literature review and experts’ opinions. Through a structured questionnaire, data were collected from PSC experts. An integrated approach of analytic hierarchy process (AHP) and interpretive structural modelling (ISM) is proposed to prioritize and examine the underlying relationship between inhibitors. This study explores the driving and dependence power of the inhibitors. The results indicate that most of the SCS inhibitors, such as institutional pressures (laws and regulations), strategic lack of strategic supplier alliance, market threat, act as drivers of SCC inhibitors, such as technological complexity, horizontal complexity, and complexity of customers. The study’s findings would help the supply chain managers and the petroleum sector policymakers to make better decision to overcome the challenges for achieving sustainability in PSC.
Keywords: Petroleum supply chain | Environment and sustainability | Complexity | Business strategy | Interpretive structural modelling | Performance
مقاله انگلیسی
2 Refraction seismic complementing electrical method in subsurface characterization for tunneling in soft pyroclastic, (a case study)
روش الکتریکی تکمیلی لرزه‌ای شکست در شناسایی زیرسطحی برای تونل‌زنی در آذرآواری نرم (مطالعه موردی)-2021
The paper highlights the potential drawback of mapping a single geophysical property for subsurface characterization in potential engineering sites. As an exemplary case study, we present the geophysical survey conducted along the surface projection of a tunnel in the quaternary volcanic terrain of the Main Ethiopia Rift. Initially, geoelectrical mapping involving 12 Vertical Electrical Sounding (VES) and a short Electrical Resistivity Imaging (ERI) line, was carried out. The 1D geoelectric model indicates that the formation resistivity at tunnel zone varies from 50 to 500 Ω∙m. The corresponding value on 2D model, (>350 Ω∙m), is also compatible. Based on limited available geological information, the geoelectric horizon was attributed to weathered and variably saturated ignimbrite. Following unexpected encounter during excavation, refraction seismic and core drilling were carried out for additional insights. Tomographic analysis of the seismic arrival times revealed that below a depth of 45 m, (tunnel zone), the velocity substratum is marked by a range, (1200–1800 m/s). Such low velocity range is typical of unconsolidated materials and, thus, cannot rationalize the geoelectrical attribution (ignimbrite). In a joint interpretation, the likely formation that may justify the observed range of the electrical resistivity and low P-wave velocity appears to be unwelded pyroclastic deposit (volcanic ash). Eventually, core samples from the tunnel zone confirmed the presence of thick ash flow. However, the unexpected ground conditions encountered at the early phase, due to insufficient information derived from a single geophysical parameter, caused extra cost and considerable delay.
Keywords: Integrated approach | Refraction seismic | DC resistivity | Subsurface characterization | Main Ethiopian Rift (MER)
مقاله انگلیسی
3 Design and research on automatic recognition system of sports dance movement based on computer vision and parallel computing
طراحی و تحقیق در مورد سیستم تشخیص خودکار حرکت رقص ورزشی بر اساس بینایی ماشین و محاسبات موازی-2021
Professional team sports analyst periodic analysis to obtain strategic and tactical insights into the players and the team’s behavior. Target motion analysis team, including systematic determination of hostile team weaknesses, and assess trained team performance and improvement potential. Team video analysis of the current investigation is usually based on workflow. Analysts can also use information visualization techniques to draw the trajectory of the ball and the players. Video analysis is usually a time-consuming process requiring analysts to remember and comment on the scene. In contrast, typically rely on visual data generally used abstract visual mapping model abstraction. They are no longer directly linked to the observed motion context but obtained from the raw tracking data. The proposal provides low-level functions and advanced motion recognition rate by another parallel motion analysis module used as input to evaluate the dancers’ performance. Application of computer vision techniques to extract the appropriate data from the track of the video input. Besides, advance besides and motion analysis technology can be used to derive and regional analysis, event analysis and correlation analysis of team players’ motion analysis measurements for football analysis. Our system is a visual mode that allows video and analysts to take advantage of both research forms. Some experts on the team sports analyst survey showed the effectiveness of this integrated approach.
Keywords: Experience modeling | Foreground detection | The mixture of gaussians | Computer vision and parallel computing
مقاله انگلیسی
4 Unpacking the role of primary packaging material in designing green supply chains: An integrated approach
Unpacking the role of primary packaging material in designing green supply chains: An integrated approach-2021
Due to the adverse impact of packaging materials on several ecosystems, the circular and sustainable approaches to manage packaging waste have been receiving increasing attention worldwide. Plastic pails are widely used primary packaging materials that are cost-effective, lightweight, yet long-lasting. In the present study, we pro- pose a closed-loop supply chain (CLSC) network design model to jointly optimize the decisions related to the location of collection, sorting, and recycling centers, and the quantity of plastic pales to be recycled and/or freshly produced and distributed. The proposed model is augmented with an incentive mechanism to acquire used plastic pails from customers as well as a green supplier selection procedure. In addition to the traditional objective of profit maximization, the proposed model also minimizes carbon emission, maximizes the return of used plastic pails, and prioritize suppliers for the procurement of sustainable packaging raw materials. A set of non-dominated solutions to the proposed multi-objective model are obtained using the Augmented ε-constraint method (AUGMECON). The results of AUGMECON are also compared with other methods such as weighted sum method and Augmented Tchebycheff method. It is oberseved that AUGMECON produces diverse set of pareto- optimal solutions for the considered problem. The applicability of the model is explained using an illustrative example of an adhesive manufacturer in India. Further, several managerial insights are drawn by carrying out sensitivity analysis through scenario building.
Keywords: Primary packaging | Closed-loop supply chain network design | Incentives | Supplier selection | Multi-objective optimization | Augmented ε-constraint method
مقاله انگلیسی
5 An integrated approach using CNN-RNN-LSTM for classification of fruit images
یک رویکرد یکپارچه با استفاده از CNN-RNN-LSTM برای طبقه بندی تصاویر میوه-2021
With the advancement in technology, Computer and machine vision system is getting involved in the agriculture sector for the last few years. Deep Learning is a recent advancement in the Artificial Intelligence field. In the present era, many researchers have used deep learning applications for the classification of images, and is found to be one of the emerging areas in computer vision. In the classification of fruit images, the main goal is to improve the accuracy of the classification system. The accuracy of the classifier depends on various factors like the nature of acquired images, the number of features, types of features, selection of optimal features from extracted features, and type of classifiers used. In the pro- posed article, integration of CNN, RNN, and LSTM for the classification of fruit images are defined. In this approach, CNN and RNN are employed for the development of discriminative characteristics and sequential-labels respectively. LSTM presents an explanation by integrating a memory cell to encode learning at each interval of classification. Key parameters: accuracy, F-measure, sensitivity, and specificity are applied to assess the achievement of the proposed scheme. From empirical results, it has been declared that the offered classification method provides efficient results.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Computations in Materials and Applied Engineering – 2021.
Keywords: CNN | RNN | LSTM | Integrated Approach | Fruit classification
مقاله انگلیسی
6 Modelling of supply chain disruption analytics using an integrated approach: An emerging economy example
مدل سازی تجزیه و تحلیل اختلال در زنجیره تامین با استفاده از یک رویکرد یکپارچه: یک مثال اقتصاد در حال ظهور-2021
The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human- made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated approach comprising both the Delphi method and the fuzzy analytic hierarchy process (FAHP). To test this integrated method, one of the well-known examples in industrial contexts of developing countries, the ready-made garment industry in Bangladesh is considered. To evaluate this industrial example, a sensitivity analysis is conducted to ensure the robustness and viability of the framework in practical settings. This study not only expands the literature scope of supply chain disruption risk assessment but through its application in any context or industry will reduce the impact of such disruptions and enhance the overall supply chain resilience. Consequently, these enhanced capabilities arm managers the ability to formulate relevant mitigation strategies that are robust and computationally efficient. These strategies will allow managers to take calculated decisions proactively. Finally, the results reveal that political and regulatory instability, cyclones, labor strikes, flooding, heavy rain, and factory fires are the top six disruption drivers causing disruptions to the ready-made garment industry in Bangladesh.
Keywords: Supply chain management | Disruption factors and drivers | Fuzzy analytic hierarchy process | Delphi method
مقاله انگلیسی
7 Work package-based information modeling for resource-constrained scheduling of construction projects
مدل سازی اطلاعات مبتنی بر بسته کار برای برنامه ریزی محدود منابع از پروژه های ساختمانی-2020
As an essential problem in construction management, the resource-constrained project scheduling problem (RCPSP) has been studied for decades; however, an integrated information model that fully supports the RCPSP solving process is still lacking. Though building information modeling (BIM) was proposed to meet the data requirements in the building life cycle, some scheduling and resource information are not considered in information transfers between the information model and the RCPSP mathematical model. This paper presents an integrated approach that enables fluent data flow from the information model to the RCPSP model for construction scheduling. Within this approach, a work package-based information model is proposed to capture all the required data of the RCPSP. Then, a semiautomatic method that integrates multisource data is introduced to form the proposed information model, and an adaptive data transmission method is used to support a designed multimode resource-constrained project scheduling problem (MRCPSP) model. The models and approaches are validated using the data of an actual project, demonstrating the feasibility and efficiency. This study contributes a novel integrated approach to formalizing a construction information model using a semiautomatic data integration approach, covering the information requirement and enables fluent data flow in the RCPSP solving process. Meanwhile, the work package-based information model is a successful attempt to introduce previouslygained knowledge into automatic schedule generation processes. Future work, such as extending the information model, creating new methods for RCPSP model generation, and data analytics, can bring new opportunities to apply more complex and intelligent methods in project scheduling and construction management.
Keywords: Information modeling | Data integration | Resource-constrained scheduling | Work package | Constraint programming | Optimization
مقاله انگلیسی
8 Review of banana green life throughout the food chain: From auto-catalytic induction to the optimisation of shipping and storage conditions
مروری بر عمر سبز موز در سراسر زنجیره غذایی: از القای خودکار کاتالیزوری گرفته تا بهینه سازی شرایط حمل و نقل و ذخیره سازی-2020
Banana green life (GL) is the time between harvesting and the start of natural ripening. GL could be considered as a major quality criterion, as it defines whether or not a fruit is suitable for export and marketing. The ending of GL, when climacteric crisis occurs, is characterized by autocatalytic ethylene production. Ethylene synthesis and regulation are described. The main methods for determining Gl are based on detecting the CO2 peak or a decrease in green pigments, using a spectrometer (NDVI) for the latter. Temperatures during fruit growth and Black Sigatoka disease are the main pre-harvest factors affecting GL. The former can be managed by applying the thermal sums concept and the latter by adequate field practices. The effects of the main exogenous storage parameters (storage temperature, relative humidity, concentration of ethylene and O2/CO2 ratio in the atmosphere) could be modelled in some cases. The most effective solutions for extending GL rely on either developing coatings using new preservative compounds, or designing packaging capable of controlling the temperature, CO2/O2 ratio and ethylene concentrations in the environment close to the fruits. It was demonstrated that 1-MCP is not relevant for increasing GL. A global and integrated approach involving the overall optimization of pre- and post-harvest factors needs to be applied to maintain green fruit until voluntary/artificial ripening is induced.
Keywords: Banana | Green life | Ethylene | Shipping | Storage
مقاله انگلیسی
9 Explainable AI: A Hybrid Approach to Generate Human-Interpretable Explanation for Deep Learning Prediction
هوش مصنوعی قابل توضیح: رویکرد ترکیبی برای ایجاد توضیح قابل تفسیر توسط انسان برای پیش بینی یادگیری عمیق-2020
With massive computing power and data explosion as catalysts, Artificial Intelligence (AI) has finally come out of research labs to become a ground-breaking technology. Businesses are seeing its value in a wide range of applications and therefore looking for ways to make AI an integral part of their decision-making processes. However, to trust an AI model prediction or to take downstream action based on a prediction outcome, one needs to understand the reasons for the prediction. With deep neural networks increasingly becoming the algorithm of choice for models, generation of such reasons has become more challenging. Deep neural networks are highly nested non-linear models that learn patterns in the data through complex combinations of inputs. Their complex architecture makes it very difficult to decipher the exact reasons for their prediction. Due to this lack of transparency, businesses are not able to utilize this technology in many applications. To increase the adoption of deep learning models, explainability is critical in building trust in the solution and in guiding downstream actions in business applications. In this paper we aim to create human-interpretable explanations for predictions from deep learning models. We propose a hybrid of two prior approaches, integrating clustering of the network’s hidden layer representation [2] with TREPAN decision tree [1], both of which uniquely deconstruct a neural network. Our aim is to visualize flow of information within the deep neural network using factors that make sense to humans, even if the underlying model uses more complex factors. This enables generation of human interpretable explanations (or, reasons codes) for each model outcome at an individual instance level. We demonstrate the new approach on credit card default prediction given by a deep feed forward neural network model. We compare and contrast this new integrated approach with three different approaches, based on the results we obtained from experimentation.
Keywords: Deep Learning | Neural Network | Explainable AI | TREPAN | Clustering | Reason Code | Comprehensibility | Fidelity | LIME
مقاله انگلیسی
10 Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique
ارزیابی سناریوهای تأثیرگذار بر اساس آب و هوا برای WWTP: استفاده از یک روش تشخیص الگو-2019
This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical influent data namely influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (PI) from the plant catchment area, are evaluated in this study. Following the outlier removal and missingdata imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while meeting effluent requirements.
Keywords: Wastewater treatment plant (WWTP) | Influent data | K-means clustering | Climatic data | Python
مقاله انگلیسی
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