Search results for: neural regeneration

Authors: Zongyan Li, Matt Best

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This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

Keywords: correlation analysis, F-ratio, levenberg-marquardt, MSE, NARX, neural network, optimisation

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: E. Nakamachi, K. Matsumoto, K. Yamamoto, Y. Morita, H. Sakamoto

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In recently, electromagnetic and mechanical stimulations have been recognized as the effective extracellular environment stimulation technique to enhance the defected peripheral nerve tissue regeneration. In this study, we developed a new hybrid bioreactor by adopting 50 Hz uniform alternative current (AC) magnetic stimulation and 4% strain mechanical stimulation. The guide tube for nerve regeneration is mesh structured tube made of biodegradable polymer, such as polylatic acid (PLA). However, when neural damage is large, there is a possibility that peripheral nerve undergoes necrosis. So it is quite important to accelerate the nerve tissue regeneration by achieving enhancement of nerve axonal extension rate. Therefore, we try to design and fabricate the system that can simultaneously load the uniform AC magnetic field stimulation and the stretch stimulation to cells for enhancement of nerve axonal extension. Next, we evaluated systems performance and the effectiveness of each stimulation for rat adrenal pheochromocytoma cells (PC12). First, we designed and fabricated the uniform AC magnetic field system and the stretch stimulation system. For the AC magnetic stimulation system, we focused on the use of pole piece structure to carry out in-situ microscopic observation. We designed an optimum pole piece structure using the magnetic field finite element analyses and the response surface methodology. We fabricated the uniform AC magnetic field stimulation system as a bio-reactor by adopting analytically determined design specifications. We measured magnetic flux density that is generated by the uniform AC magnetic field stimulation system. We confirmed that measurement values show good agreement with analytical results, where the uniform magnetic field was observed. Second, we fabricated the cyclic stretch stimulation device under the conditions of particular strains, where the chamber was made of polyoxymethylene (POM). We measured strains in the PC12 cell culture region to confirm the uniform strain. We found slightly different values from the target strain. Finally, we concluded that these differences were allowable in this mechanical stimulation system. We evaluated the effectiveness of each stimulation to enhance the nerve axonal extension using PC12. We confirmed that the average axonal extension length of PC12 under the uniform AC magnetic stimulation was increased by 16 % at 96 h in our bio-reactor. We could not confirm that the axonal extension enhancement under the stretch stimulation condition, where we found the exfoliating of cells. Further, the hybrid stimulation enhanced the axonal extension. Because the magnetic stimulation inhibits the exfoliating of cells. Finally, we concluded that the enhancement of PC12 axonal extension is due to the magnetic stimulation rather than the mechanical stimulation. Finally, we confirmed that the effectiveness of the uniform AC magnetic field stimulation for the nerve axonal extension using PC12 cells.

Keywords: nerve cell PC12, axonal extension, nerve regeneration, electromagnetic-mechanical stimulation, bioreactor

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Authors: M. Karzhauov, А. Mukhambetova, M. Sarsenova, E. Raimagambetov, V. Ogay

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Regeneration of injured articular cartilage remains one of the most difficult and unsolved problems in traumatology and orthopedics. Currently, for the treatment of cartilage defects surgical techniques for stimulation of the regeneration of cartilage in damaged joints such as multiple microperforation, mosaic chondroplasty, abrasion and microfractures is used. However, as shown by clinical practice, they can not provide a full and sustainable recovery of articular hyaline cartilage. In this regard, the current high hopes in the regeneration of cartilage defects reasonably are associated with the use of tissue engineering approaches to restore the structural and functional characteristics of damaged joints using stem cells, growth factors and biopolymers or scaffolds. The purpose of the present study was to investigate the effects of chondroinductive growth factors and synovium-derived mesenchymal stem cells (SD-MSCs) on the regeneration of cartilage defects in rabbits. SD-MSCs were isolated from the synovium membrane of Flemish giant rabbits, and expanded in complete culture medium α-MEM. Rabbit SD-MSCs were characterized by CFU-assay and by their ability to differentiate into osteoblasts, chondrocytes and adipocytes. The effects of growth factors (TGF-β1, BMP-2, BMP-4 and IGF-I) on MSC chondrogenesis were examined in micromass pellet cultures using histological and biochemical analysis. Articular cartilage defect (4mm in diameter) in the intercondylar groove of the patellofemoral joint was performed with a kit for the mosaic chondroplasty. The defect was made until subchondral bone plate. Delivery of SD-MSCs and growth factors was conducted in combination with hyaloronic acid (HA). SD-MSCs, growth factors and control groups were compared macroscopically and histologically at 10, 30, 60 and 90 days aftrer intra-articular injection. Our in vitro comparative study revealed that TGF-β1 and BMP-4 are key chondroinductive factors for both the growth and chondrogenesis of SD-MSCs. The highest effect on MSC chondrogenesis was observed with the synergistic interaction of TGF-β1 and BMP-4. In addition, biochemical analysis of the chondrogenic micromass pellets also revealed that the levels of glycosaminoglycans and DNA after combined treatment with TGF-β1 and BMP-4 was significantly higher in comparison to individual application of these factors. In vivo study showed that for complete regeneration of cartilage defects with intra-articular injection of SD-MSCs with HA takes time 90 days. However, single injection of SD-MSCs in combiantion with TGF-β1, BMP-4 and HA significantly promoted regeneration rate of the cartilage defects in rabbits. In this case, complete regeneration of cartilage defects was observed in 30 days after intra-articular injection. Thus, our in vitro and in vivo study demonstrated that combined application of rabbit SD-MSC with chondroinductive growth factors and HA results in strong synergistic effect on the chondrogenesis significantly enhancing regeneration of the damaged cartilage.

Keywords: Mesenchymal stem cells, synovium, chondroinductive factors, TGF-β1, BMP-2, BMP-4, IGF-I

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Authors: Rodrigo S. Moreira, Nelson F. F. Ebecken

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This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs.

Keywords: ram memory, WiSARD weightless neural network, object tracking, quantization

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Jin Lin, Shouxiang Lu, Kim Meow Liew

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The requirement for the long-term mission of the submarine and spacecraft has made the removal of CO₂ and trace CO the critical technology to ensure the health and life of the crews. In this work, CuMnCe, a metal oxide catalyst, supporting K₂CO₃ sorbent was prepared by the wet-solid state impregnation method to realize the integrated CO and CO₂ removal, which might also reduce the volume/mass load of the purification units in the limited space. The as-prepared samples with different addition amount of K₂CO₃ were tested using the fixed bed reactor to reveal the CO oxidation and CO₂ absorption behavior. And the regeneration and stability experiments were also conducted. The results showed that the samples realized the catalyst and sorbent integration to capture CO and CO₂ at the same time. The addition amount of the sorbent had a weak influence on the CO oxidation performance. While the addition amount affected the CO₂ sorption efficiency and capacity significantly. Meanwhile, the presence of water vapor could reduce the CO oxidation activity of the samples similarly, whether with K2CO3 sorbent addition or not. Furtherly, regeneration and stability experiment results showed that the samples after 3-5 times regeneration exhibited almost the same performance of CO and CO₂ removal. Summarily, CuMnCe catalyst supporting K₂CO₃ sorbent could be a good attempt to control CO and CO₂ pollutants generated from the daily equipment running and staff breathing in the confined space such as submarine and spacecraft.

Keywords: CO oxidation, CO₂ absorptio, potassium carbonate, CuMnCe metal oxide, confined space

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Authors: Ying Su, Morgan C. Wang

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Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN).

Keywords: automated machines learning, autoregressive integrated moving average, neural networks, time series analysis

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Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: Nisha Khatik, Ramesh Joshi

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An in vitro plant regeneration system was developed via direct somatic embryogenesis from different seedling explants of an important medicinal plant Murraya koenigii (L) Spreng. Cotyledons (COT), Hypocotyle (HYP)(10 to 15 mm) and Root (RT) segments (10 to 20 mm) were excised from 60 days old seedlings as explants. The somatic embryos induction was achieved on MS basal medium augmented with different concentrations of BAP 1.33 to 8.40 µM and TDZ 1.08 to 9.82 µM. The globular embryos originated from cut ends and entire surface of the root, hypocotyle explants and margins of cotyledons within 30-40days. The percentage of somatic embryos induction per explant was significantly higher in HYP explants (94.21±5.77%) in the MS basal medium supplemented with 6.20 µM BAP and 8.64 µM TDZ. The highest rate of conversion of torpedo, heart and cotyledonary stages from globular stage was obtained in MS medium supplemented with 8.64 µM TDZ. The matured somatic embryos were transferred to the MS basal medium without PGRs. Highest 88% of the matured embryos were germinated on transfer to the PGR free medium where they grew for a further 3-4 weeks. Out of seventy six hardened plants seventy (92%) plantlets were found healthy under field conditions.

Keywords: Murraya koenigii, somatic embryogenesis, thidiazuron, regeneration, rutaceae

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Raffaello Furlan

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In the 21st century, the sustainable urban development of GCC-cities is challenged by inhabitants’ over-dependency on private-use vehicles. In turn, this habit has generated problems of urban inefficiency, contributing to traffic congestion, pollution, urban sprawling, fragmentation of the urban fabric, and various environmental and social challenges. In the context of Doha, the capital city of the State of Qatar, the over-dependency on private-use vehicles is justified by the lack of alternative public modes of transportation that support the need to connect fragmented urban districts and provide an effective solution to urban sprawl. Therefore, the current construction of the Qatar Metro Rail is offering the potential for investigating and defining a strategy for the sustainable urban development and/or urban regeneration of transit villages (TODs) in Qatar. Namely, the aim of this research study is (i) to investigate the development of transit villages (TODs) in the cultural-heritage district of Msheireb, Downtown Doha, (ii) to explore how the introduction of the new public transport system of Doha Metro can be effectively utilized as means of urban regeneration of the cultural core of the city, (iii) to propose a masterplan for TOD suitable for the district, suiting and responding to regional cultural and societal values. The findings reveal that the strategies for the sustainable urban regeneration of Msheireb are based on (i) the integration of land-use and multimodal transportation systems, (ii) the implementation of the public realm, and (iii) conservation of culture and urban identity.

Keywords: sustainable urbanism, smart growth, TODs, cultural district, Msheireb Downtown Doha

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Hsin Hua He

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The purpose of this study is to strengthen the social cohesion of the local community by injecting the cultural and creative concept into the industrial heritage transformation. The paradigms of industrial heritage research tend to explore from the perspective of space analysis, which concerned less about the cultural regeneration and the development of local culture. The paradigms of cultural quarter research use to from the perspective of creative economy and urban planning, concerned less about the social impacts and the interaction between residents and industrial sites. This research combines these two research areas of industrial heritage and cultural quarter, and focus on the social and cultural aspects. The transformation from the industrial heritage into a cultural park not only enhances the cultural capital and the quality of residents’ lives, but also preserves the unique local values. Internally it shapes the local identity, while externally establishes the image of the city. This paper uses Westergasfabriek Cultural Park in Amsterdam as the case study, through literature analysis, field work, and depth interview to explore how the cultural regeneration transforms industrial heritage. In terms of the planners’ and residents’ point of view adopt the theory of community participation, social capital, and sense of place to analyze the social impact of the industrial heritage transformation. The research finding is through cultural regeneration policies like holding cultural activities, building up public space, social network and public-private partnership, and adopting adaptive reuse to fulfil the people’s need and desire and reach the social cohesion. Finally, the study will examine the transformation of Taiwan's industrial heritage into cultural and creative quarters. The results are expected to use the operating experience of the Amsterdam cases and provide directions for Taiwan’s industrial heritage management to meet the cultural, social, economic symbiosis.

Keywords: cultural regeneration, community participation, social capital, sense of place, industrial heritage transformation

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: Hoo Cheol Lee, Dae Seung Kim, Sang Myung Jung, Gwang Heum Yoon, Hwa Sung Shin

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Loss of bone tissue can occur due to a bone tissue disease and aging or fracture. Renewable formation of bone is mainly made by its differentiation and metabolism. For this reason, osteoblasts have been studied for regeneration of bone tissue. So, tissue engineering has attracted attention as a recovery means. In tissue engineering, a particularly important factor is a scaffold that supports cell growth. For osteoblast scaffold, we used the cellulose nanowhisker (CNW) extracted from marine organism. CNW is one of an abundant material obtained from a number of plants and animals. CNW is polymer consisting of monomer cellulose and this composition offers biodegradability and biocompatibility to CNW. Mechanical strength of CNW is superior to the existing natural polymers. In addition, substances of marine origin have a low risk of secondary infection by bacteria and pathogen in contrast with those of land-derived. For evaluating its suitability as an osteoblast scaffold, we fabricate CNW film for osteoblast culture and performed the MTT assay and ALP assay to confirm its cytotoxicity and effect on differentiation. Taking together these results, we assessed CNW is a potential candidate of a material for bone tissue regeneration.

Keywords: bone regeneration, cellulose nanowhisker, marine derived material, osteoblast

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Pham-Thi Huong, Byeong-Kyu Lee, Chi-Hyeon Lee, Jitae Kim

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In this study synthesized Cu-nano-zeolite was evaluated for its potential use in CO2 capture. The specific surface area of Cu-nano zeolite was measured as 869.32 m2/g with a pore size of 3.86 nm. The adsorption capacity of CO2 by Cu-nano zeolite was decreased with increasing temperature. The identified adsorption capacity of CO2 by Cu-nano zeolite was 7.16 mmol/g at a temperature of 20 oC and at pressure of 1 atm. The adoption selectivity of CO2 over N2 strongly depend on the temperature and the highest selectivity by Cu-nano zeolite was 50.71 at 20 oC. From analysis of regeneration characteristics of CO2 loaded adsorbent, the percentage removal of CO2 was maintained at more than 78.2 % even after 10 cycles of adsorption-desorption. Based on these result, the Cu-nano zeolite can be used as an effective and economical adsorbent for CO2 capture.

Keywords: CO2 capture, selectivity, Cu-nano zeolite, regeneration.

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

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Authors: Sadiq J. Abou-Loukh

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The present work deals with implementation of Petri nets, which own the perfect ability of modeling, are used to establish a fault diagnosis model. Fault diagnosis of a control system received considerable attention in the last decades. The formalism of representing neural networks based on Petri nets has been presented. Neural Petri Net (NPN) reasoning model is investigated and developed for the fault diagnosis process of electric control system. The proposed NPN has the characteristics of easy establishment and high efficiency, and fault status within the system can be described clearly when compared with traditional testing methods. The proposed system is tested and the simulation results are given. The implementation explains the advantages of using NPN method and can be used as a guide for different online applications.

Keywords: petri net, neural petri net, electric control system, fault diagnosis

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Authors: Mazifah Simis, Azahan Awang, Kadir Arifin

Abstract:

The brownfield regeneration is being implemented in developped countries. However, as a group 1 developing country in the South East Asia, the rapid development and increasing number of urban population in Malaysia have urged the needs to incorporate the brownfield regeneration into its physical planning development. The increasing number of urban ex-landfills is seen as a new resource that could overcome the issues of inadequate urban green space provisions. With regards to the new development approach in urban planning, this perception study aims to identify the sustainable planning approach based on what the stakeholders have in mind. Respondents consist of 375 local communities within four urban ex-landfill areas and 61 landscape architect and town planner officers in the Malaysian Local Authorities. Three main objectives are set to be achieved, which are (i) to identify ex-landfill issues that need to be overcome prior to the ex-landfill redevelopment (ii) to identify the most suitable types of ex-landfill redevelopment, and (iii) to identify the priority function for ex-landfill redevelopment as the public parks. From the data gathered through the survey method, the order of priorities based on stakeholders' perception was produced. The results show different perception among the stakeholders, but they agreed to the development of the public park as the main development. Hence, this study attempts to produce an integrated approach as a model for sustainable ex-landfill redevelopment that could be accepted by the stakeholders as a beneficial future development that could change the image of 296 ex-landfills in Malaysia into the urban public parks by the year 2020.

Keywords: brownfield regeneration, ex-landfill redevelopment, integrated approach, stakeholders' perception

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Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Grippin Akeng, Suresh Kumar Muniandy, Nor Aini Ab Shukor

Abstract:

Plant regeneration was achieved from shoot tip and nodal segment of Gonystylus bancanus (Miq) Kurz. cultured in Murashige and Skoog’s medium supplemented with various concentrations of 6-benzylaminopurine (BAP). The most optimum concentration of BAP for shoot initiation is 10.0 mgl⁻¹ with approximately 10% of shoot tip and 15% of nodal segment produced single shoot after 28 and 15 days of culture incubation respectively. Rooting was achieved when shoots were transferred into MS medium supplemented with 5.0 mgl⁻¹ Naphthalene acetic acid (NAA). Synthesizing results developed through this research can be a starting point for the upscalling and optimization process in future.

Keywords: gonystylus bancanus, organogenesis, shoot initiation, shoot tip

Procedia PDF Downloads 215