Relevant bibliographies by topics / Automatic distillation of structure

Academic literature on the topic 'Automatic distillation of structure'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Automatic distillation of structure"

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Luo, Ling, Dingyu Xue, and Xinglong Feng. "Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation." Electronics 9, no. 9 (August 19, 2020): 1337. http://dx.doi.org/10.3390/electronics9091337.

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Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness, which plagues the working-age population. Automatic medical imaging diagnosis provides a non-invasive method to assist ophthalmologists in timely screening of suspected DR cases, which prevents its further deterioration. However, the state-of-the-art deep-learning-based methods generally have a large amount of model parameters, which makes large-scale clinical deployment a time-consuming task. Moreover, the severity of DR is associated with lesions, and it is difficult for the model to focus on these regions. In this paper, we propose a novel deep-learning technique for grading DR with only image-level supervision. Specifically, we first customize the model with the help of self-knowledge distillation to achieve a trade-off between model performance and time complexity. Secondly, CAM-Attention is used to allow the network to focus on discriminative zone, e.g., microaneurysms, soft/hard exudates, etc.. Considering that directly attaching a classifier after the Side branch will disrupt the hierarchical nature of convolutional neural networks, a Mimicking Module is employed that allows the Side branch to actively mimic the main branch structure. Extensive experiments are conducted on two benchmark datasets, with an AUC of 0.965 and an accuracy of 92.9% for the Messidor dataset and 67.96% accuracy achieved for the challenging IDRID dataset, which demonstrates the superior performance of our proposed method.
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Liu, Ning, Yan Li, and Wan Jie Sun. "Study on Plate Washer Sewage Treatment." Applied Mechanics and Materials 312 (February 2013): 546–49. http://dx.doi.org/10.4028/www.scientific.net/amm.312.546.

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To study the plate washer sewage treatment, the mature theories and structures in other sectors were introduced the printing industry. Plate washer is a kind of printing auxiliary equipment, mainly used for the printing plate cleaning and washing wastewater treating and recycling, the sewage treatment system including filtration part and distillation part, filtering part which is based on design of automatic self-cleaning filter have two stage filtration system, distillation part use crude oil distillation tower as the distillation system model.
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Sutijan, Sutijan, Megan Jobson, and Robin Smith. "Synthesis of Ternary Homogeneous Azeotropic Distillation Sequences: Entrainer Selection." ASEAN Journal of Chemical Engineering 12, no. 1 (August 6, 2012): 20. http://dx.doi.org/10.22146/ajche.49752.

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This paper presents a methodology for automatic selection of entrainers for separating binary azeotropic mixtures using homogeneous azeotropic distillation. A new classification system for ternary mixtures based on the termini of distillation boundaries and the type (stability) of products and azeotropes is proposed. The new characterisation system is able to link candidate entrainers to flowsheet structures which can facilitate the separation. Existing entrainer selection criteria are extended to accommodate other promising entrainers, including light, intermediate and heavy-boiling entrainers.
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Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.
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Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.
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6

Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.
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7

Xiao, Da, Tianyu Huang, Yihao Li, Chang Liu, and Fuquan Zhang. "A Lightweight Human Action Classification Method for Green IoT Sport Applications." Journal of Sensors 2022 (July 1, 2022): 1–16. http://dx.doi.org/10.1155/2022/4102552.

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This paper proposes a lightweight human action classification method for Green Internet of Things (IoT) sport applications. This method classifies the human motion data collected by wearables or other IoT devices with energy-efficient techniques, by enabling a small number of sample training and incremental classification to achieve the purpose of energy-efficient. To lessen the complexity of the model and reduce the number of samples required for parameter estimation, we propose a shared Hidden Conditional Random Field (sHCRF) model. The sHCRF model adds a shared-classification layer structure to reduce the parameter computation. In the experiments, the classification accuracy of the sHCRF model is above 95%. This paper introduces an incremental learning method based on knowledge distillation. The new model suppresses the forgetting of existing classification knowledge while fitting new data to learn new classification knowledge. In the incremental scenarios, the classification accuracy of the sHCRF model is above 70%. The experimental results show that this method can lightly implement convenient and fast automatic classification of action acquisition.
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Sivananaithaperumal, Sudalaiandi, and Subramanian Baskar. "Design Of Multivariable Fractional Order Pid Controller Using Covariance Matrix Adaptation Evolution Strategy." Archives of Control Sciences 24, no. 2 (June 1, 2014): 235–51. http://dx.doi.org/10.2478/acsc-2014-0014.

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Abstract This paper presents an automatic tuning of multivariable Fractional-Order Proportional, Integral and Derivative controller (FO-PID) parameters using Covariance Matrix Adaptation Evolution Strategy (CMAES) algorithm. Decoupled multivariable FO-PI and FO-PID controller structures are considered. Oustaloup integer order approximation is used for the fractional integrals and derivatives. For validation, two Multi-Input Multi- Output (MIMO) distillation columns described byWood and Berry and Ogunnaike and Ray are considered for the design of multivariable FO-PID controller. Optimal FO-PID controller is designed by minimizing Integral Absolute Error (IAE) as objective function. The results of previously reported PI/PID controller are considered for comparison purposes. Simulation results reveal that the performance of FOPI and FO-PID controller is better than integer order PI/PID controller in terms of IAE. Also, CMAES algorithm is suitable for the design of FO-PI / FO-PID controller.
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Cai, Qiwen, Ran Chen, Lu Li, Chao Huang, Haisu Pang, Yuanshi Tian, Min Di, et al. "The Application of Knowledge Distillation toward Fine-Grained Segmentation for Three-Vessel View of Fetal Heart Ultrasound Images." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–7. http://dx.doi.org/10.1155/2022/1765550.

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Objectives. Measuring anatomical parameters in fetal heart ultrasound images is crucial for the diagnosis of congenital heart disease (CHD), which is highly dependent on the clinical experience of the sonographer. To address this challenge, we propose an automated segmentation method using the channel-wise knowledge distillation technique. Methods. We design a teacher-student architecture to conduct channel-wise knowledge distillation. ROI-based cropped images and full-size images are used for the teacher and student models, respectively. It allows the student model to have both the fine-grained segmentation capability inherited from the teacher model and the ability to handle full-size test images. A total of 1,300 fetal heart ultrasound images of three-vessel view were collected and annotated by experienced doctors for training, validation, and testing. Results. We use three evaluation protocols to quantitatively evaluate the segmentation accuracy: Intersection over Union (IoU), Pixel Accuracy (PA), and Dice coefficient (Dice). We achieved better results than related methods on all evaluation metrics. In comparison with DeepLabv3+, the proposed method gets more accurate segmentation boundaries and has performance gains of 1.8% on mean IoU (66.8% to 68.6%), 2.2% on mean PA (79.2% to 81.4%), and 1.2% on mean Dice (80.1% to 81.3%). Conclusions. Our segmentation method could identify the anatomical structure in three-vessel view of fetal heart ultrasound images. Both quantitative and visual analyses show that the proposed method significantly outperforms the related methods in terms of segmentation results.
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Krasikova, Raisa N., and Viktoriya V. Orlovskaya. "Phase Transfer Catalysts and Role of Reaction Environment in Nucleophilc Radiofluorinations in Automated Synthesizers." Applied Sciences 12, no. 1 (December 29, 2021): 321. http://dx.doi.org/10.3390/app12010321.

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Incorporation of [18F]fluorine into PET radiotracer structure has traditionally been accomplished via nucleophilic pathways. The [18F]fluoride is generated in an aqueous solution via proton irradiation of oxygen-18 enriched water and must to be introduced into water-free organic solutions in order to generate reactive species. Thus nucleophilic 18F-fluorination traditionally included steps for [18F]fluoride concentration on the anion exchange resin, followed by removal of residual water via azeotropic distillation with MeCN, a time-consuming process associated with radioactivity losses and difficult automation. To circumvent this, several adsorption/elution protocols were developed based on the minimization of water content in traditional kryptofix-based [18F]fluoride eluents. The use of pre-dried KOH/kryptofix solutions, tertiary alcohols, and strong organic bases was found to be effective. Advances in transition metal-mediated SNAr approaches for radiolabeling of non-activated aromatic substrates have prompted development of alternative techniques for reactive [18F]fluoride species generation, such as organic solutions of non-basic alkyl ammonium and pyridinium sulfonates, etc. For radiofluorinations of iodonium salts precursors, a “minimalist” approach was introduced, avoiding the majority of pitfalls common to more complex methods. These innovations allowed the development of new time-efficient and convenient work-up procedures that are easily implementable in modern automated synthesizers. They will be the subject of this review.
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Dissertations / Theses on the topic "Automatic distillation of structure"

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Takase, Hiroshi. "Systematic Structure Synthesis of Distillation-Based Separation Processes." Kyoto University, 2018. http://hdl.handle.net/2433/232063.

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LeThanh, Huong. "Automatic discourse structure generation using rhetorical structure theory." Thesis, Middlesex University, 2004. http://eprints.mdx.ac.uk/8002/.

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This thesis addresses a difficult problem in text processing: creating a System to automatically derive rhetorical structures of text. Although the rhetorical structure has proven to be useful in many fields of text processing such as text summarisation and information extraction, Systems that automatically generate rhetorical structures with high accuracy are difficult to find. This is beccause discourse is one of the biggest and yet least well defined areas in linguistics. An agreement amongst researchcrs on the best method for nnalysing thc rhetorical structure of text has not been found. This thesis focuses on investigating a method to generate the rhetorical structures of text. By exploiting different cohesive devices, it proposes a method to recognise rhetorical relations between spans by checking for the appearance of these devices. These factors include cue phrases, noun-phrase cues, verb-phrase cues, reference words, time references, substitution words, ellipses, and syntactic information. The discourse analyser is divided into two levels: sentence-level and text-level. The former uses syntactic information and cue phrases to segment sentences into elementary discourse units and to generate a rhetorical structure for each sentence. The latter derives rhetorical relations between large spans and then replaces each sentence by its corresponding rhetorical structure to produce the rhetorical structure of text. The rhetorical structure at the text-level is derived by selecting rhetorical relations to connect adjacent and non-overlapping spans to form a discourse structure that covers the entire text. Constraints of textual organisation and textual adjacency are effectively used in a beam search to reduce the search space in generating such rhetorical structures. Experiments carried out in this research received 89.4% F-score for the discourse segmentation, 52.4% F-score for the sentence-level discourse analyser and 38.1% F-score for the final output of the System. It shows that this approach provides good performance cumparison with current research in discourse.
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Albaret, Christian. "Contribution à l'étude et à la commande des colonnes de distillation multiconstituants." Phd thesis, École Nationale Supérieure des Mines de Paris, 1992. http://pastel.archives-ouvertes.fr/pastel-00838235.

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Nous présentons dans un premier temps un cadre de construction de modèles de colonne de distillation prenant en compte de manière générale une large classe de modèles thermo- dynamiques pour un nombre quelconque de constituants et le comportement hydrodynamique des plateaux. Après un rappel des résultats de la littérature pour les colonnes binaires, nous démontrons un résultat d'existence et d'unicité du point stationnaire d'un modèle de colonne à 'débits molaires constants' à un nombre quelconque de constituants et un résultat d'inversion de l'équilibre thermodynamique. Nous définissons la notion de fonction 'simplexe-relative' pour décrire les propriétés de fonctions d'un simplexe sur lui-même construites à partir des modèles thermodynamiques. Les résultats s'appuient sur la théorie du degré topologique appliquée à ces fonctions. Nous présentons ensuite une extension de la méthode de réduction de modèle par agrégation et son utilisation pour la commande. Nous testons la commande obtenue en simulation sur un modèle de colonne de distillation industrielle à haute pureté.
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Leitch, Megan. "Quantitative Structure-Flux Relationships of Membrane Distillation Materials for Water Desalination." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/780.

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Membrane distillation (MD) is an emergent water desalination technology with potential for scalable, sustainable production of fresh water from highly concentrated brines. Wider adoption of MD technology depends upon improvements to process efficiency. In recent years, researchers have published a number of experimental papers seeking to improve mass and heat transport properties of MD membranes. However, an imperfect understanding of how intrinsic membrane geometry affects MD performance limits efforts to optimize membrane structure. The objective of this dissertation is to help elucidate effects of membrane structure on MD flux, permeability, and thermal performance, with a focus on novel fibrous membranes. Mechanistic and empirical modeling methods were employed to relate the structural characteristics of bacterial nanocellulose and electrospun polymeric membranes to experimentally-measured MD performance. Through these experimental and modeling studies, three conclusions are reached. First, the MD community can hasten the search for optimal membrane structures by improving the quality and reproducibility of reported experimental data. Review of published and newly-collected MD data shows that feed and permeate stream channel geometry and flow non-idealities can substantially affect measured performance metrics for MD membranes. If these factors are accounted for by careful characterization of convective heat transfer coefficients, membrane permeability and thermal efficiency can be definitively deduced. A new methodology is presented for determining convective heat transfer coefficient using experimentally-validated Nusselt correlations. Accurate reporting of cassette heat transfer metrics will facilitate inter-study experimental reproducibility and comparison. Second, use of dimensional analysis to empirically model MD transport is effective for predicting vapor flux in fibrous membranes. Advantages of the model include its use of easily-measurable structural parameters tailored specifically for fibrous membranes and the incorporation of all relevant vapor, membrane, and system characteristics into a mathematically simple, yet theoretically sound, regression model. The new model predicts MD flux more accurately than the mechanistic Dusty Gas Model or previously published empirical MD models. Dimensional-analysis-based transport models may be generalizable for a variety of novel membrane types, lead to a more rigorous understanding of structural influences on vapor transport processes, and guide the development of high-performance membrane structures. Finally, MD process efficiency can benefit by development of highly porous, scalable membrane materials. Bacterial nanocellulose aerogel membranes exhibit substantial improvements in intrinsic permeability and thermal efficiency as compared to traditional phase-inversion membranes, suggesting that there is an opportunity to advance MD process viability through improved membrane design. By mimicking the porosity and pore-interconnectivity of nanocellulose aerogels, novel membrane materials can achieve high thermal efficiency and low mass transport resistance. This dissertation contributes experimental data and modeling techniques to improve knowledge of membrane structural effects on MD performance. These contributions have implications for the wider adoption of MD technology through better reproducibility of published experimental results, enhanced transport modeling to optimize membrane structure, and demonstrated thermal efficiency of a highly porous materials.
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Karanikola, Vasiliki, Andrea F. Corral, Hua Jiang, A. Eduardo Sáez, Wendell P. Ela, and Robert G. Arnold. "Effects of membrane structure and operational variables on membrane distillation performance." ELSEVIER SCIENCE BV, 2017. http://hdl.handle.net/10150/623056.

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A bench-scale, sweeping gas, flat-sheet Membrane Distillation (MD) unit was used to assess the importance of membrane architecture and operational variables to distillate production rate. Sweeping gas membrane distillation (SGMD) was simulated for various membrane characteristics (material, pore size, porosity and thickness), spacer dimensions and operating conditions (influent brine temperature, sweep gas flow rate and brine flow rate) based on coupled mass and energy balances. Model calibration was carried out using four membranes that differed in terms of material selection, effective pore size, thickness and porosity. Membrane tortuosity was the lone fitting parameter. Distillate fluxes and temperature profiles from experiments matched simulations over a wide range of operating conditions. Limitations to distillate production were then investigated via simulations, noting implications for MD design and operation. Under the majority of conditions investigated, membrane resistance to mass transport provided the primary limitation to water purification rate. The nominal or effective membrane pore size and the lumped parameter epsilon/delta tau (porosity divided by the product of membrane tortuosity and thickness) were primary determinants of membrane resistance to mass transport. Resistance to Knudsen diffusion dominated membrane resistance at pore diameters <0.3 mu m. At larger pore sizes, a combination of resistances to intra-pore molecular diffusion and convection across the gas-phase boundary layer determined mass transport resistance. Findings are restricted to the module design flow regimes considered in the modeling effort. Nevertheless, the value of performance simulation to membrane distillation design and operation is well illustrated.
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O'Hanlon, Ken. "Automatic music transcription using structure and sparsity." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8818.

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Automatic Music Transcription seeks a machine understanding of a musical signal in terms of pitch-time activations. One popular approach to this problem is the use of spectrogram decompositions, whereby a signal matrix is decomposed over a dictionary of spectral templates, each representing a note. Typically the decomposition is performed using gradient descent based methods, performed using multiplicative updates based on Non-negative Matrix Factorisation (NMF). The final representation may be expected to be sparse, as the musical signal itself is considered to consist of few active notes. In this thesis some concepts that are familiar in the sparse representations literature are introduced to the AMT problem. Structured sparsity assumes that certain atoms tend to be active together. In the context of AMT this affords the use of subspace modelling of notes, and non-negative group sparse algorithms are proposed in order to exploit the greater modelling capability introduced. Stepwise methods are often used for decomposing sparse signals and their use for AMT has previously been limited. Some new approaches to AMT are proposed by incorporation of stepwise optimal approaches with promising results seen. Dictionary coherence is used to provide recovery conditions for sparse algorithms. While such guarantees are not possible in the context of AMT, it is found that coherence is a useful parameter to consider, affording improved performance in spectrogram decompositions.
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Duchnowski, Paul. "A new structure for automatic speech recognition." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/17333.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Includes bibliographical references (leaves 102-110).
by Paul Duchnowski.
Sc.D.
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Schrimpf, Natalie Margaret. "Effects of Topic Structure on Automatic Summarization." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10957338.

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Automatic summarization involves finding the most important information in a text in order to create a reduced version of that text that conveys the same meaning as the original. In this dissertation, I present a method for using topic information to influence which content is selected for a summary.

This dissertation addresses questions such as how to represent the meaning of a document for automatic tasks. For tasks such as automatic summarization, there is a tradeoff between using sophisticated linguistic methods and using methods that can easily and efficiently be used by automatic systems. This research seeks to find a balance between these two goals by using linguistically-motivated methods that can be used to improve automatic summarization performance. Another question addressed in this work is the balance between summary coverage and length. A summary must be long enough to convey the information from the original text but short enough to be useful in place of the original document. This dissertation explores the use of topics to increase coverage while reducing redundancy.

There are several issues that affect summary quality. These include information coverage, redundancy, and coherence. This dissertation focuses on achieving coverage of all distinct concepts in a text by incorporating topic structure. During the summarization process, emphasis is placed on including information from all topics in order to produce summaries that cover the range of information present in the original documents. In this work, several notions of what constitutes a topic are explored, with particular focus on defining topics using information from Rhetorical Structure Theory (Mann and Thompson 1988). The results of incorporating topics into a summarization system show that topic structure improves automatic summarization performance.

The contributions of this dissertation include demonstrating that focusing on coverage of the different topics in a text improves summaries, and topic structure is an effective way to achieve this coverage. This research also shows the effectiveness of a simple modular method for incorporating topics into summarization that allows for comparison of different notions of topic and summarization techniques.

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Murugesan, Viyash. "Optimization of Nanocomposite Membrane for Membrane Distillation." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36534.

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In this study, effects of nanoparticles, including 7 nm TiO2, 200 nm TiO2, and hydrophilic and hydrophobic SiO2 with mean diameter in the range of 15–20 nm and their concentration on the membrane properties and vacuum membrane distillation (VMD) performance were evaluated. The effect of membrane thickness and support materials were also investigated. The membranes were characterised extensively in terms of morphology (SEM), water contact angle, water liquid entrance pressure (LEPw), surface roughness, and pore size. While the best nanocomposite membranes with 200 nm TiO2 Nanoparticles(NPs) were obtained at 2% particle concentration, the optimal particle concentration was 5% when 7 nm TiO2 was integrated. Using nanocomposite membrane containing 2 wt% TiO2 – 200 nm nanoparticles, VMD flux of 2.1 kg/m2h and LEPw of 34 PSI was obtained with 99% salt rejection. Furthermore, it was observed that decreasing the membrane thickness would increase the portion of finger-like layer in membrane and reduce the spongy-like layer when hydrophilic nanoparticles were used. Using continuous flow VMD, a flux of 3.1 kg/m2h was obtained with neat PVDF membranes, which was 600% higher than the flux obtained by the static flow VMD with the same membrane at the same temperature and vacuum pressure. The fluxes of both static and flow-cell VMD increased with temperature. Furthermore, it was evident that the continuous flow VMD at 2 LPM yielded 300% or higher flux than static VMD at any given temperature, indicating strong effects of turbulence provided in the flow-cell VMD.
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Hillard, Dustin Lundring. "Automatic sentence structure annotation for spoken language processing /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/6080.

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Books on the topic "Automatic distillation of structure"

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Bakatselos, Christos. Automatic learning of paths in a hypermedia structure. Manchester: UMIST, 1995.

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Androutsos, Peter Panagiotis. Automatic structure and fault detection of semiconductor micrograph images. Ottawa: National Library of Canada, 1999.

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Vladimir, Krejnin German, ed. Pneumatic actuating systems for automatic equipment: Structure and design. Boca Raton: Taylor & Francis, 2006.

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1952-, Garofalo Franco, and Glielmo Luigi 1960-, eds. Robust control via variable structure and Lyapunov techniques. London: Springer, 1996.

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Franklin, Tom. The changing retail banking industry structure. Norwalk, CT: Business Communications Co., Inc., 1996.

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Emelʹi͡anov, Stanislav Vasilʹevich. Variable-structure control systems: Discrete and digital. Moscow: Mir Publishers, 1995.

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M, Taylor M., Néel F, and Bouwhuis Don G, eds. The Structure of multimodal dialogue II. Philadelphia: J. Benjamins Pub. Co., 2000.

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IEEE International Workshop on Variable Structure Systems (2006 Alghero, Italy). 2006 International workshop on variable structure systems: Alghero Sardinia, Italy, 5-7 June 2006. Piscataway, N.J: IEEE, 2006.

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Huo, Yu Xing, and Xu Jian-Xin, eds. Advances in variable structure systems: Analysis, integration and applications : proceedings of the 6th IEEE International Workshop on Variable Structure Systems : Gold Coast, Queensland, Australia, 7-9 December 2000. Singapore: River Edge, N.J., 2000.

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-F, Lafay J., and International Federation of Automatic Control., eds. System structure and control 1998 (SSC'98): A proceedings volume from the 5th IFAC Conference, Nantes, France, 8-10 July 1998. Oxford, Eng: Published for the International Federation of Automatic Control by Pergamon, 1998.

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Book chapters on the topic "Automatic distillation of structure"

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Tyreus, B. D. "Selection of Controller Structure." In Practical Distillation Control, 178–91. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-0277-4_9.

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Renard, D. "Automatic Structure Recognition." In Geostatistics, 579–90. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-6844-9_45.

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Chen, Zailiang, Xianxian Zheng, Hailan Shen, Ziyang Zeng, Yukun Zhou, and Rongchang Zhao. "Improving Knowledge Distillation via Category Structure." In Computer Vision – ECCV 2020, 205–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58604-1_13.

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Li, Bo, Bin Chen, Yunxiao Wang, Tao Dai, Maowei Hu, Yong Jiang, and Shutao Xia. "Knowledge Distillation via Channel Correlation Structure." In Knowledge Science, Engineering and Management, 357–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82136-4_29.

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Hisgen, Débora, and Daniela López De Luise. "Dialog Structure Automatic Modeling." In Advances in Artificial Intelligence, 69–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16761-4_7.

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Lou, Yinghou, Yanan Jiang, and Jin Liu. "Structure Design for Automatic Conveying." In Perspectives from Europe and Asia on Engineering Design and Manufacture, 771–78. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2212-8_75.

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Schneider, Martin, and Reinhard Klein. "Semi-Automatic Digital Landform Mapping." In Landform - Structure, Evolution, Process Control, 37–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-75761-0_3.

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McFarlane, I. "Products with preserved structure." In Automatic Control of Food Manufacturing Processes, 171–96. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2187-7_6.

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Cao, Yang, Ruoyu Yan, Jun Tan, and Shijie Cai. "Automatic Interpretation of Construction Structure Drawings." In Graphics Recognition Recent Advances, 298–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40953-x_26.

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Nnaji, Bartholomew O. "Structure of an automatic robot programmer." In Theory of Automatic Robot Assembly and Programming, 67–92. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1590-2_4.

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Conference papers on the topic "Automatic distillation of structure"

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Manikandan, P., P. Thangaganapathy, and D. Manamalli. "Selection of control structure and dual composition control for ethanol-water distillation column." In 2017 Trends in Industrial Measurement and Automation (TIMA). IEEE, 2017. http://dx.doi.org/10.1109/tima.2017.8064814.

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Zhang, Zhexi, Wei Zhu, Junchi Yan, Peng Gao, and Guotong Xie. "Automatic Student Network Search for Knowledge Distillation." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412192.

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Ahmed, Waqar, Andrea Zunino, Pietro Morerio, and Vittorio Murino. "Compact CNN Structure Learning by Knowledge Distillation." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413006.

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Pourmohseni, Behnaz, Michael Glas, and Jurgen Teich. "Automatic operating point distillation for hybrid mapping methodologies." In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2017. http://dx.doi.org/10.23919/date.2017.7927160.

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Fukuda, Takashi, and Samuel Thomas. "Mixed Bandwidth Acoustic Modeling Leveraging Knowledge Distillation." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003760.

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Orihashi, Shota, Yoshihiro Yamazaki, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, and Ryo Masumura. "Hierarchical Knowledge Distillation for Dialogue Sequence Labeling." In 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2021. http://dx.doi.org/10.1109/asru51503.2021.9687959.

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"Design of Control Structure for Binary Distillation Column." In 3rd International Conference on Advances in Engineering Sciences and Applied Mathematics. International Institute of Engineers, 2015. http://dx.doi.org/10.15242/iie.e0315071.

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Hansen, J. E. "Control structure selection for energy integrated distillation column." In UKACC International Conference on Control. Control '96. IEE, 1996. http://dx.doi.org/10.1049/cp:19960604.

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Wang, Xinyu, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, and Kewei Tu. "Structure-Level Knowledge Distillation For Multilingual Sequence Labeling." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-main.304.

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Bao, Yuanyuan, Yang Li, Liqiu Ma, and Wai Chen. "Synchronous automatic training for wearable sensors via knowledge distillation." In IPSN '19: The 18th International Conference on Information Processing in Sensor Networks. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3302506.3312600.

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Reports on the topic "Automatic distillation of structure"

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Hendrickson, Aidan, and Philippe Pierre Pebay. Recommendations on a Document Structure Format for Automatic Report Generation. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1376817.

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Olsen, Mari B., Bonnie Dorr, and Scott Thomas. Enhancing Automatic Acquisition of Thematic Structure in a Large-Scale Lexicon for Mandarian Chinese. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada458646.

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, in­cluding the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.
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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.

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The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center of INDOT developed a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and classification of vehicles involved in an incident. The research team designed the system, including the hardware and software components added to the existing INDOT CCTV system; the relationship between the added system and the currently existing INDOT system; the database structure for traffic data extracted from the videos; and a user-friendly, web-based server for showing the incident locations automatically. The specific work in this project includes vehicle-detection, road boundary detection, lane detection, vehicle count over time, flow-rate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The preliminary prototype of some system components has been implemented in the Development of Automated Incident Detection System Using Existing ATMS CCT (SPR-4305).
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Raymond, Kara, Laura Palacios, and Evan Gwilliam. Status of climate and water resources at Big Bend National Park: Water year 2019. Edited by Tani Hubbard. National Park Service, September 2022. http://dx.doi.org/10.36967/2294267.

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Climate and hydrology are major drivers of ecosystem structure and function, particularly in arid and semi-arid ecosystems. Understanding changes in climate, groundwater, streamflow, and water quality is central to assessing the condition of park resources. This report combines data collected on climate, groundwater, and springs at Big Bend National Park (NP) to provide an integrated look at climate and water conditions during water year (WY) 2019 (October 2018–September 2019). However, this report does not address the Rio Grande or its tributaries. Annual precipitation was higher than normal (1981–2010) for Big Bend NP at four of the five National Oceanic and Atmospheric Administration Cooperative Observer Program weather stations: 111% of normal for Chisos Basin, 122% of normal for Panther Junction, 155% of normal for Persimmon Gap, and 124% of normal for Rio Grande Village. Castolon had 88% of normal annual precipitation. All five stations had higher than normal rainfall in October and December, while rainfall totals were substantially below normal at all stations in November, February, and March. Monthly precipitation totals for April through September were more variable from station to station. Mean monthly maximum air temperatures were below normal in the fall months, with Panther Junction as much as 7.5°F below normal in October. Monthly temperatures from January through July were more variable. Temperatures in August and September were warmer than normal at every station, up to +9.4°F at Rio Grande Village and +8.7°F at Chisos Basin in July. The reconnaissance drought index values indicate generally wetter conditions (based on precipitation and evaporative demand) at Chisos Basin since WY2016 and at Panther Junction and Persimmon Gap since WY2015, except for WY2017. This report presents the manual and automatic groundwater monitoring results at nine wells. Five wells had their highest water level in or just before WY2019: Panther Junction #10 peaked at 99.94 ft below ground surface (bgs) in September 2018, Contractor’s Well peaked at 31.43 ft bgs in November 2018, T-3 peaked at 65.39 ft bgs in December 2018, K-Bar #6 Observation Well peaked at 77.78 ft bgs in February 2019, and K-Bar #7 Observation Well peaked at 43.18 ft bgs in February 2019. This was likely in response to above normal rainfall in the later summer and fall 2018. The other monitoring wells did not directly track within-season precipitation. The last measurement at Gallery Well in WY2019 was 18.60 ft bgs. Gallery Well is located 120 feet from the river and closely tracked the Rio Grande stage, generally increasing in late summer or early fall following higher flow events. Water levels in Gambusia Well were consistently very shallow, though the manual well measurement collected in April was 4.25 ft bgs—relatively high for the monitoring record—and occurred outside the normal peak period of later summer and early fall. The last manual measurement taken at TH-10 in WY2019 was 34.80 ft bgs, only 0.45 ft higher than the earliest measurement in 1967, consistent with the lack of directional change in groundwater at this location, and apparently decoupled from within-season precipitation patterns. The last water level reading in WY2019 at Oak Springs #1 was 59.91 ft bgs, indicating an overall decrease of 26.08 ft since the well was dug in 1989. The Southwest Network Collaboration (SWNC) collects data on sentinel springs annually in the late winter and early spring following the network springs monitoring protocol. In WY2019, 18 sentinel site springs were visited at Big Bend NP (February 21, 2019–March 09, 2019). Most springs had relatively few indications of natural and anthropogenic disturbances. Natural disturbances included recent flooding, drying, and wildlife use. Anthropogenic disturbances included flow modifications (e.g., springboxes), hiking trails, and contemporary human use. Crews observed one to seven facultative/obligate wetland plant...
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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