Search results for: waste classification

Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

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The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Ranjit Kumar Sahu, Ramakar Jha

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Urban Water Management is the practice of managing freshwater, waste water, and storm water as components of a basin-wide management plan. It builds on existing water supply and sanitation considerations within an urban settlement by incorporating urban water management within the scope of the entire river basin. The pervasive problems generated by urban development have prompted, in the present work, to study the spatial extent of urbanization in Golden Triangle of Odisha connecting the cities Bhubaneswar (20.2700° N, 85.8400° E), Puri (19.8106° N, 85.8314° E) and Konark (19.9000° N, 86.1200° E)., and patterns of periodic changes in urban development (systematic/random) in order to develop future plans for (i) urbanization promotion areas, and (ii) urbanization control areas. Remote Sensing, using USGS (U.S. Geological Survey) Landsat8 maps, supervised classification of the Urban Sprawl has been done for during 1980 - 2014, specifically after 2000. This Work presents the following: (i) Time series analysis of Hydrological data (ground water and rainfall), (ii) Application of SWMM (Storm Water Management Model) and other soft computing techniques for Urban Water Management, and (iii) Uncertainty analysis of model parameters (Urban Sprawl and correlation analysis). The outcome of the study shows drastic growth results in urbanization and depletion of ground water levels in the area that has been discussed briefly. Other relative outcomes like declining trend of rainfall and rise of sand mining in local vicinity has been also discussed. Research on this kind of work will (i) improve water supply and consumption efficiency (ii) Upgrade drinking water quality and waste water treatment (iii) Increase economic efficiency of services to sustain operations and investments for water, waste water, and storm water management, and (iv) engage communities to reflect their needs and knowledge for water management.

Keywords: Storm Water Management Model (SWMM), uncertainty analysis, urban sprawl, land use change

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Authors: Mohd Moiz Khan, S. M. Mahajani, G. N. Jadhav

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Waste foundry sand (total clay content 15%) contains toxic heavy metals and particulate matter which make dumping of waste sand an environmental and health hazard. Disposal of waste foundry sand (WFS) remains one of the substantial challenges faced by Indian foundries nowadays. To cope up with this issue, the chemical method was used to reclaim WFS. A stirrer tank reactor was used for chemical reclamation. Experiments were performed to reduce the total clay content from 15% to as low as 0.9% in chemical reclamation. This method, although found to be effective for WFS reclamation, it may face a challenge due to the possibly high operating cost. Reclaimed sand was found to be satisfactory in terms of sand qualities such as total clay (0.9%), active clay (0.3%), acid demand value (ADV) (2.6%), loss on igniting (LOI) (3 %), grain fineness number (GFN) (56), and compressive strength (60 kPa). The experimental data generated on chemical reactor under different conditions is further used to optimize the design and operating parameters (rotation speed, sand to acidic solution ratio, acid concentration, temperature and time) for the best performance. The use of reclaimed sand within the foundry would improve the economics and efficiency of the process and reduce environmental concerns.

Keywords: chemical reclamation, clay content, environmental concerns, recycle, waste foundry sand

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

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Authors: Paola Villoria Saez, Mercedes Del Rio Merino, Jaime Santacruz Astorqui, Cesar Porras Amores

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In construction the Circular Economy covers the whole cycle of the building construction: from production and consumption to waste management and the market for secondary raw materials. The circular economy will definitely contribute to 'closing the loop' of construction product lifecycles through greater recycling and re-use, helping to build a market for reused construction materials salvaged from demolition sites, boosting global competitiveness and fostering sustainable economic growth. In this context, this paper presents the latest research of 'Waste to resources (W2R)' project funded by the Spanish Government, which seeks new solutions to improve energy efficiency in buildings by developing new building materials and products that are less expensive, more durable, with higher quality and more environmentally friendly. This project differs from others as its main objective is to reduce to almost zero the Construction and Demolition Waste (CDW) generated in building rehabilitation works. In order to achieve this objective, the group is looking for new ways of CDW recycling as raw materials for new conglomerate materials. With these new materials, construction elements reducing building energy consumption will be proposed. In this paper, the results obtained in the project are presented. Several tests were performed to gypsum samples containing different percentages of CDW waste generated in Spanish building retroffiting works. Results were further analyzed and one of the gypsum composites was highlighted and discussed. Acknowledgements: This research was supported by the Spanish State Secretariat for Research, Development and Innovation of the Ministry of Economy and Competitiveness under 'Waste 2 Resources' Project (BIA2013-43061-R).

Keywords: building waste, CDW, gypsum, recycling, resources

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: R. Ziaie Moayed, S. P. Emadoleslami Oskoei, S. D. Beladi Mousavi, A. Taleb Beydokhti

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There have been many reports of the destructive effects of cement on the environment in recent years. In the present research, it has been attempted to reduce the destructive effects of cement by replacing silica fume as adhesive materials instead of cement. The present study has attempted to improve the mechanical properties of cement slurry by using waste material from a glass production factory, located in Qazvin city of Iran, in which accumulation volume has become an environmental threat. The chemical analysis of the waste material indicates that this material contains about 94% of SiO₂ and AL₂O₃ and has a close structure to silica fume. Also, the particle grain size test was performed on the mentioned waste. Then, the unconfined compressive strength test of the slurry was performed by preparing a mixture of water and adhesives with different percentages of cement and silica fume. The water to an adhesive ratio of this mixture is 1:3, and the curing process last 28 days. It was found that the sample had an unconfined compressive strength of about 300 kg/cm² in a mixture with equal proportions of cement and silica fume. Besides, the sample had a brittle fracture in the slurry sample made of pure cement, however, the fracture in cement-silica fume slurry mixture is flexible and the structure of the specimen remains coherent after fracture. Therefore, considering the flexibility that is achieved by replacing this waste, it can be used to stabilize soils with cracking potential.

Keywords: cement replacement, cement slurry, environmental threat, natural pozzolan, silica fume, waste material

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Authors: Chia-Hau Liu, Tai-Yue Wang

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Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Reiner-Ace P. Mateo, Vince Paolo F. Obille

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The traditional method of classifying the potential mineral resources requires a significant amount of time and money. In this paper, an alternative way to classify potential mineral resources with GIS application in Surigao del Sur. The three (3) analog map data inputs integrated to GIS are geologic map, topographic map, and land cover/vegetation map. The indicators used in the classification of potential nickel laterite integrated from the analog map data inputs are a geologic indicator, which is the presence of ultramafic rock from the geologic map; slope indicator and the presence of plateau edges from the topographic map; areas of forest land, grassland, and shrublands from the land cover/vegetation map. The potential mineral of the area was classified from low up to very high potential. The produced mineral potential classification map of Surigao del Sur has an estimated 4.63% low nickel laterite potential, 42.15% medium nickel laterite potential, 43.34% high nickel laterite potential, and 9.88% very high nickel laterite from its ultramafic terrains. For the validation of the produced map, it was compared with known occurrences of nickel laterite in the area using a nickel mining tenement map from the area with the application of remote sensing. Three (3) prominent nickel mining companies were delineated in the study area. The generated potential classification map of nickel-laterite in Surigao Del Sur may be of aid to the mining companies which are currently in the exploration phase in the study area. Also, the currently operating nickel mines in the study area can help to validate the reliability of the mineral classification map produced.

Keywords: mineral potential classification, nickel laterites, GIS, remote sensing, Surigao del Sur

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Authors: Murat Iskender Aktaş, Sadi Ergin, Rasime Acar Aktaş

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In the light of fast-paced changes and developments in the health sector, the Ministry of Health started a new structuring with decree law numbered 663 within the scope of the Project of Transformation in Health. Accordingly, hospitals should ensure patient satisfaction through more efficient, more effective use of resources and sustainable finance by placing patients in the centre and should operate to increase efficiency to its maximum level while doing these. Within this study, in order to find out how efficient the hospitals were in terms of medical waste management between the years 2011-2014, the data from six hospitals of Amasya Public Hospitals Union were evaluated separately through Data Envelopment Analysis (DEA) method. First of all, input variables were determined. Input variables were the number of patients admitted to polyclinics, the number of inpatients in clinics, the number of patients who were operated and the number of patients who applied to the laboratory. Output variable was the cost of medical wastes in Turkish liras. Each hospital’s total medical waste level before and after public hospitals union; the amounts of average medical waste per patient admitted to polyclinics, per inpatient in clinics, per patient admitted to laboratory and per operated patient were compared within each group. In addition, average medical waste levels and costs were compared for Turkey in general and Europe in general. Paired samples t-test was used to find out whether the changes (increase-decrease) after public hospitals union were statistically significant. The health facilities that were unsuccessful in terms of medical waste management before and after public hospital union and the factors that caused this failure were determined. Based on the results, for each health facility that was ineffective in terms of medical waste management, the level of improvement required for each input was determined. The results of the study showed that there was an improvement in medical waste management applications after the health facilities became a member of public hospitals union; their medical waste levels were lower than the average of Turkey and Europe while the averages of cost of disposal were the highest.

Keywords: medical waste management, cost of medical waste, public hospitals, data envelopment analysis

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Authors: Ali A. Hashim Habib, Ahmed A. Abdel-Rehim

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Assuredly, massive amounts of wastes that are not utilized and dumped in uncontrolled dumpsites will be one of the major sources of diseases, fires, and emissions. With easy steps and minimum effort, energy can be produced from these gases. The present work introduces an experimental and theoretical analysis to estimate the amount of landfill gas and the corresponding energy which can be produced based on actual Egyptian municipal wastes composition. Two models were utilized and compared, EPA (Environmental Protection Agency) model and CDM (Clean Development Mechanisms) model to estimate methane generation rates and total CH4 emissions based on a particular landfill. The results showed that for every ton of municipal waste, 140 m3 of landfill gas can be produced. About 800 kW of electricity for a minimum of 24 years can be generated form one million ton of municipal waste. A total amount of 549,025 ton of carbon emission can be avoided during these 24 years.

Keywords: energy from landfill gases, landfill biogas, methane emission, municipal solid waste, renewable energy sources

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Authors: Aprajita Singh, Anamika Tripathi, Surya P. Dwivedi

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Moradabad is a small town in the Northern area of Uttar Pradesh, India. It is situated on the bank of river Ramganga which is also known as ‘Brass City of India’. There is eventually increase in the environmental pollution due to uncontrolled and inappropriate e-waste burning (recycling) activities which have been reported in many areas of Moradabad. In this paper, analysis of toxic heavy metals, causing pollution to the surrounding environment released from the e-waste burning and much other recycling process. All major e-waste burning sites are situated on the banks of the river which is burned in open environmental conditions. Soil samples were collected from seven (n=3) different sites including control site, after digestion of soil samples using triacid mixture, analysis of different toxic metals (Pb, Ar, Hg, Cd, Cr, Cu, Zn, Fe, and Ni) has been carried out with the help of instrument ICP-AAS. After the study, the outcome is that the soil of those areas contains a relatively high level of the toxic metals in order of Cu>Fe>Pb>Cd>Cr>Zn>Ar>Hg. The concentration of Cd, Pb, Cr, Ar and Zn (the majority of samples experimentally proved) exceeded the maximum standard level of WHO. Sequentially this study showed that uncontrolled e-waste processing operations caused serious pollution to local soil and release of toxic metals in the environment is also causing adverse effect on the health of people living in the nearby areas making them more prone to various harmful diseases.

Keywords: brass city, environment pollution, e-waste, toxic heavy metals

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Authors: Paola Villoria Sáez, Mercedes Del Río Merino, Jaime Santa Cruz Astorqui, Antonio Rodríguez Sánchez

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The Spanish construction industry generates large volumes of waste. However, despite the legislative improvements introduced for construction and demolition waste (CDW), construction waste recycling rate remains well below other European countries and also below the target set for 2020. This situation can be due to many difficulties. i.e.: The difficulty of onsite segregation or the estimation in advance of the total amount generated. Despite these difficulties, the proper management of CDW must be one of the main aspects to be considered by the construction companies. In this sense, some large national companies are implementing Integrated Management Systems (IMS) including not only quality and safety aspects, but also environment issues. However, although this fact is a reality for large construction companies still the vast majority of companies need to adopt this trend. In short, it is common to find in small and medium enterprises a decentralized management system: A single system of quality management, another for system safety management and a third one for environmental management system (EMS). In addition, the EMSs currently used address CDW superficially and are mainly focus on other environmental concerns such as carbon emissions. Therefore, this research determines and implements a specific best practice management system for CDW based on eight procedures in a Spanish Construction company. The main advantages and drawbacks of its implementation are highlighted. Results of this study show that establishing and implementing a CDW management system in building works, improve CDW quantification as the company obtains their own CDW generation ratio. This helps construction stakeholders when developing CDW Management Plans and also helps to achieve a higher adjustment of CDW management costs. Finally, integrating this CDW system with the EMS of the company favors the cohesion of the construction process organization at all stages, establishing responsibilities in the field of waste and providing a greater control over the process.

Keywords: construction and demolition waste, waste management, best practices, waste minimization, building, quality management systems

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Authors: T. Tommasi, E. Batuecas, G. Mancini, G. Saracco, D. Fino

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Extra virgin olive-oil (EVO) production is an important economic activity for several countries, especially in the Mediterranean area such as Spain, Italy, Greece and Tunisia. The two major by-products from olive oil production, solid-liquid Olive Pomace (OP) and the Olive Mill Waste Waters (OMWW), are still mainly disposed on soil, in spite of the existence of legislation which already limits this practice. The present study compares the environmental impacts associated with two different scenarios for the management of waste from olive oil production through a comparative Life Cycle Assessment (LCA). The two alternative scenarios are: (I) Anaerobic Digestion and (II) current Disposal on soil. The analysis was performed through SimaPro software and the assessment of the impact categories was based on International Life Cycle Data and Cumulative Energy Demand methods. Both the scenarios are mostly related to the cultivation and harvesting phase and are highly dependent on the irrigation practice and related energy demand. Results from the present study clearly show that as the waste disposal on soil causes the worst environmental performance of all the impact categories here considered. Important environmental benefits have been identified when anaerobic digestion is instead chosen as the final treatment. It was consequently demonstrated that anaerobic digestion should be considered a feasible alternative for olive mills, to produce biogas from common olive oil residues, reducing the environmental burden and adding value to the olive oil production chain.

Keywords: anaerobic digestion, waste management, agro-food waste, biogas

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Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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Authors: Dasom Kim, William Xiu Shun Wong, Yoonjin Hyun, Donghoon Lee, Minji Paek, Sungho Byun, Namgyu Kim

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There have been many researches on document classification for classifying voluminous documents automatically. Through document classification, we can assign a specific category to each unlabeled document on the basis of various machine learning algorithms. However, providing labeled documents manually requires considerable time and effort. To overcome the limitations, the semi-supervised learning which uses unlabeled document as well as labeled documents has been invented. However, traditional document classifiers, regardless of supervised or semi-supervised ones, cannot sufficiently explain the reason or the process of the classification. Thus, in this paper, we proposed a methodology to visualize major topics and class components of each document. We believe that our methodology for visualizing topics and classes of each document can enhance the reliability and explanatory power of document classifiers.

Keywords: data mining, document classifier, text mining, topic modeling

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Daniela Zalazar-Garcia, Erick Torres, German Mazza

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Slow pyrolysis of a pellet of pistachio waste was studied using a lab-scale stainless-steel reactor. Experiments were conducted at different heating rates (5, 10, and 15 K/min). A 3-E (environmental, exergetic, and energetic) analysis for the processing of 20 kg/h of bio-waste was carried out. Experimental results showed that biochar and gas yields decreased with an increase in the heating rate (43 to 36 % and 28 to 24 %, respectively), while the bio-oil yield increased (29 to 40 %). Finally, from the 3-E analysis and the experimental results, it can be suggested that an increase in the heating rate resulted in a higher pyrolysis exergetic efficiency (70 %) due to an increase of the bio-oil yield with high-energy content.

Keywords: 3E assessment, bio-waste pellet, life cycle assessment, slow pyrolysis

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Authors: Hsin-Yang Wang, K. M. A. Salam, Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao

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Detecting user intent from natural language expression has a wide variety of use cases in different natural language processing applications. Recently few-shot training has a spike of usage on commercial domains. Due to the lack of significant sample features, the downstream task performance has been limited or leads to an unstable result across different domains. As a state-of-the-art method, the pre-trained BERT model gathering the sentence-level information from a large text corpus shows improvement on several NLP benchmarks. In this research, we are proposing a method to change multi-class classification tasks into binary classification tasks, then use the confidence score to rank the results. As a language model, BERT performs well on sequence data. In our experiment, we change the objective from predicting labels into finding the relations between words in sequence data. Our proposed method achieved 71.0% accuracy in the internal intent detection dataset and 63.9% accuracy in the HuffPost dataset. Acknowledgment: This work was supported by NCKU-B109-K003, which is the collaboration between National Cheng Kung University, Taiwan, and SoftBank Corp., Tokyo.

Keywords: OSML, BERT, text classification, one shot

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Authors: Anusha Chanda, Reenu Singh

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Packaging is one of the major contributors to global waste. While efforts are being made to switch to more sustainable types of packaging, such as switching from single use plastics to paper, not all polluting materials, have been rethought in terms of recycling. Minimalism in packaging design can help reduce the amount of waste produced greatly. While online companies have shifted to using cardboard boxes for packages, a large amount of waste in still generated from other materials affiliated with cardboard packaging, such as tape, bubble wrap, plastic wrap, among others. Minimalism also works by reducing extra packaging and increasing the reusability of the material. This paper looks at research related to minimalism in packaging design, minimalism, and sustainability. A survey was conducted in order to find out the different ways in which minimalism can be implemented in packaging design. Information gathered from the research and responses from the survey was used to ideate product design alternatives for sustainable substitutes for bubble wrap in packaging. This would help greatly reduce the amount of packaging waste and improve environmental quality.

Keywords: environment, minimalism, packaging, product design, sustainable

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Authors: Mahima D, Sini T

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The use of geocell in subgrade has been previously studied by various researchers in the past. It was observed that the infill material used could affect the performance of the geocell reinforced subgrade. So, the use of waste materials as infill in geocell reinforced subgrade may prove to be more effective, economical, and environment-friendly. The performance of demolition waste as an infill was studied using flexure testing, and we compared the results with that of the other infill materials; soil and sand. Flexural behaviour is very important to the geosynthetic application in pavements as it acts as a the geocell reinforcement acts as flexible layer embedded in pavements and leads to an improvement in stress distribution and reduction in stress on the soil subgrade. The flexural behaviour was determined using four-point bending tests and results were expressed in terms of modulus improvement factor (MIF) and load-deflection behaviour. The geocell reinforced subgrade with different infill materials was tested for flexural behaviour in a polywood-polywood three-layered beam model. The deflections of the three-layered model beam were measured for the corresponding load increments. Elastic modulus of the soil-geocell composite was calculated using closed-form solutions. Geocells were prepared from geonets with three different aspect ratios 0.45, 0.67, and 1. The demolition waste infilled geocell mattress with aspect ratio 0.67 showed improved flexural behavior with MIF of 2.67 followed by soil and sand. Owing to its improved flexural resistance as seen from the MIF and load-deflection behivour, crushed demolition waste can be effectively used as infill material for geocell reinforced subgrade, thereby reducing the difficulties in the management of demolition waste and improving the load distribution of weaker subgrade.

Keywords: demolition waste, flexural behavior, geocell, modulus improvement factor

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Authors: Spoorthi Sripad

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Remote sensing is a powerful tool for acquiring information about the Earth's surface without direct contact, relying on the interaction of electromagnetic radiation with various materials and features. This paper explores the fundamental principle of "Interaction with Earth's Surface" in remote sensing, shedding light on the intricate processes that occur when electromagnetic waves encounter different surfaces. The absorption, reflection, and transmission of radiation generate distinct spectral signatures, allowing for the identification and classification of surface materials. The paper delves into the significance of the visible, infrared, and thermal infrared regions of the electromagnetic spectrum, highlighting how their unique interactions contribute to a wealth of applications, from land cover classification to environmental monitoring. The discussion encompasses the types of sensors and platforms used to capture these interactions, including multispectral and hyperspectral imaging systems. By examining real-world applications, such as land cover classification and environmental monitoring, the paper underscores the critical role of understanding the interaction with the Earth's surface for accurate and meaningful interpretation of remote sensing data.

Keywords: remote sensing, earth's surface interaction, electromagnetic radiation, spectral signatures, land cover classification, archeology and cultural heritage preservation

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Zin Mar Lwin

Abstract:

Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Oriyomi M. Okeyinka, David A. Oloke, Jamal M. Khatib

Abstract:

Large quantities of solid wastes being generated worldwide from sources such as household, domestic, industrial, commercial and construction demolition activities, leads to environmental concerns. Utilization of these wastes in making building construction materials can reduce the magnitude of the associated problems. When these waste products are used in place of other conventional materials, natural resources and energy are preserved and expensive and/or potentially harmful waste disposal is avoided. Recycling which is regarded as the third most preferred waste disposal option, with its numerous environmental benefits, stand as a viable option to offset the environmental impact associated with the construction industry. This paper reviews the results of laboratory tests and important research findings, and the potential of using these wastes in building construction materials with focus on sustainable development. Research gaps, which includes; the need to develop standard mix design for solid waste based building materials; the need to develop energy efficient method of processing solid waste use in concrete; the need to study the actual behavior or performance of such building materials in practical application and the limited real life application of such building materials have also been identified. Therefore a research is being proposed to develop an environmentally friendly, lightweight building block from recycled waste paper, without the use of cement, and with properties suitable for use as walling unit. This proposed research intends to incorporate, laboratory experimentation and modeling to address the identified research gaps.

Keywords: recycling, solid wastes, construction, building materials

Procedia PDF Downloads 364

Authors: Isiya Aminu Dabai, Adebola Kayode, Adeosun Kayode Daniel

Abstract:

Rural water supply relies mainly on groundwater exploitation, because it is more accessible, reliable, cheaper to develop and maintain, also with good quality compared to the surface water. Despite these advantages, groundwater has come under pollution threats like waste dumps, mineral exploitation, industrialization etc. This study investigates the effects of an open dumping to the surrounding groundwater. Ten hand dug well water samples were collected from the surroundings and tested. The average result shows that temperature, colour and turbidity to be 8.50 c, 6.1 TCU and 3.1 NTU respectively and pH, conductivity, total dissolved solids, chloride content and hardness to be 7.2, 4.78, 1.8, 5.7, and 3.4 respectively while in the bacteriological test well no. 1, 2, 3, and 5 shows the presence of coliforms and E. Coli bacteria.

Keywords: groundwater, pollution, waste, dump site, unsafe, quality

Procedia PDF Downloads 660

Authors: A. A. Ajayi-Banji, M. A. Adegbile, T. D. Akpenpuun, J. Bello, O. Omobowale, D. A. Jenyo

Abstract:

Ceramic ware waste applicability as coarse aggregate was considered in this study for dense masonry unit production. The waste was crushed into 1.4 mm particle size and mixed with natural fine aggregate in the ratio 2:3. Portland ordinary cement, aggregate, and water mix ratio was 1:7:0.5. Masonry units produced were cured for 7, 21 and 28 days prior to compressive test. The result shows that curing age have a significant effect on all the compressive strength indices inspected except for Young’s modulus. Crushing force and the compressive strength of the ceramic-natural fine aggregate blocks increased by 11.7 – 54.7% and 11.6 – 59.2% respectively. The highest ceramic-natural fine block compressive strength at yield and peak, 4.97 MPa, was obtained after 21 days curing age. Ceramic aggregate introduced into the dense blocks improved the suitability of the blocks for construction purposes.

Keywords: ceramic ware waste, co-ballast, dense masonry unit, compressive strength, curing time

Procedia PDF Downloads 384

Authors: Feten Chihi, Gabriella Varga

Abstract:

The world's most prevalent waste management strategy is landfills. However, it grew more difficult due to a lack of acceptable waste sites. In order to develop larger landfills and extend their lifespan, the purpose of this article is to expand the capacity of the construction by varying the slope's inclination and to examine its effect on the safety factor. The capacity change with tilt is mathematically determined. Using a new probabilistic calculation method that takes into account the heterogeneity of waste layers, the safety factor for various slope angles is examined. To assess the effect of slope variation on the overall safety of landfills, over a hundred computations were performed for each angle. It has been shown that capacity increases significantly with increasing inclination. Passing from 1:3 to 2:3 slope angles and from 1:3 to 1:2 slope angles, the volume of garbage that can be deposited increases by 40 percent and 25 percent, respectively, of the initial volume. The results of the safety factor indicate that slopes of 1:3 and 1:2 are safe when the standard method (homogenous waste) is used for computation. Using the new approaches, a slope with an inclination of 2:3 can be deemed safe, despite the fact that the calculation does not account for the safety-enhancing effect of daily cover layers. Based on the study reported in this paper, the malty layered nonhomogeneous calculating technique better characterizes the safety factor. As it more closely resembles the actual state of landfills, the employed technique allows for more flexibility in design parameters. This work represents a substantial advance in limiting both safe and economical landfills.

Keywords: landfill, municipal solid waste, slope inclination, capacity, safety factor

Procedia PDF Downloads 173