Search results for: radial network

Authors: Chutikarn Sriwiboon, Suwaree Yordchim

Abstract:

Benjarong handicraft product is one of the most important handicraft products from Thailand. It involves the management of traditional wisdom of arts and Thai culture. This paper drew upon data collection from local communities by using an in-depth interview technique which was conducted in Thailand during summer of 2014. The survey was structured primarily to obtain local wisdom and concerns toward their career development. This research paper was a qualitative research conducted by focus groups with a total of 51 cooperative women and occupational groups around Thailand which produced the Benjarong products. The data were significantly collected from many sources and many communities, which totaled 24,430 handicraft products, in which the 668 different patterns of Benjarong products were produced by 51 local community network groups in Thailand. The findings revealed that after applying the Philosophy of Sufficiency Economy, there was a significantly positive change in their career development and the process of knowledge management enables local community to enhance their personal development and career.

Keywords: Benjarong, career development, community, handicraft

Procedia PDF Downloads 383

Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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Authors: Mohsen Maadani, Seyed Ahmad Motamedi

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The use of wireless technology in industrial networks has gained vast attraction in recent years. In this paper, we have thoroughly analyzed the effect of contention window (CW) size on the performance of IEEE 802.11-based industrial wireless networks (IWN), from delay and reliability perspective. Results show that the default values of CWmin, CWmax, and retry limit (RL) are far from the optimum performance due to the industrial application characteristics, including short packet and noisy environment. An adaptive CW algorithm (payload-dependent) has been proposed to minimize the average delay. Finally a simple, but effective CW and RL setting has been proposed for industrial applications which outperforms the minimum-average-delay solution from maximum delay and jitter perspective, at the cost of a little higher average delay. Simulation results show an improvement of up to 20%, 25%, and 30% in average delay, maximum delay and jitter respectively.

Keywords: average delay, contention window, distributed coordination function (DCF), jitter, industrial wireless network (IWN), maximum delay, reliability, retry limit

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Authors: Provides Ng

Abstract:

Platform economy is a peer-to-peer model of distributing resources facilitated by community-based digital platforms. In recent years, digital platforms are rapidly reconfiguring the public realm using hyper-personalisation techniques. This paper aims at investigating how urban planning can leapfrog into the digital age to help relieve the rising tension of the global issue of labour flow; it discusses the means to transfer techniques of hyper-personalisation into urban planning for plasticity using platform technologies. This research first denotes the limitations of the current system of urban residency, where the system maintains itself on the circulation of documents, which are data on paper. Then, this paper tabulates how some of the institutions around the world, both public and private, digitise data, and streamline communications between a network of systems and citizens using platform technologies. Subsequently, this paper proposes ways in which hyper-personalisation can be utilised to form a digital planning platform. Finally, this paper concludes by reviewing how the proposed strategy may help to open up new ways of thinking about how we affiliate ourselves with cities.

Keywords: platform urbanism, hyper-personalisation, digital inventory, urban accessibility

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Authors: Tomas Menard

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The design of control-law for engineering systems has been investigated for many decades. While many results are concerned with continuous systems with continuous output, nowadays, many controlled systems have to transmit their output measurements through network, hence making it discrete-time. But it is well known that the sampling of a system whose control-law is based on the continuous output may render the system unstable, especially when this sampling period is long compared to the system dynamics. The control design then has to be adapted in order to cope with this issue. In this paper, we consider systems which can be modeled as double integrator with uncertainty on the input since many mechanical systems can be put under such form. We present a control scheme based on an observer using only discrete time measurement and which provides continuous time estimation of the state, combined with a continuous control law, which stabilized a system with second-order dynamics even in the presence of uncertainty. It is further shown that arbitrarily long sampling periods can be dealt with properly setting the control scheme parameters.

Keywords: dynamical system, control law design, sampled output, observer design

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Z. Gharaee

Abstract:

Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Brian Ceh, Alison Jackson-Holland

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Food deserts are a reality in some cities. These deserts can be described as a shortage of healthy food options within close proximity of consumers. The shortage in this case is typically facilitated by a lack of stores in an urban area that provide adequate fruit and vegetable choices. This study explores new avenues to better understand food deserts by examining modes of transportation that are available to shoppers or consumers, e.g. walking, automobile, or public transit. Further, this study is unique in that it not only explores the location of large grocery stores, but small grocery and convenience stores too. In this study, the relationship between some socio-economic indicators, such as personal income, are also explored to determine any possible association with food deserts. In addition, to help facilitate our understanding of food deserts, complex network spatial models that are built on adequate algorithms are used to investigate the possibility of food deserts in the city of Hamilton, Canada. It is found that Hamilton, Canada is adequate serviced by retailers who provide healthy food choices and that the food desert phenomena is almost absent.

Keywords: Canada, desert, food, Hamilton, store

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Authors: Andrea N. Ofori-Boadu

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The purpose is to develop a conceptual model of subcontractor development practices and processes that strengthen the integration of subcontractors into construction supply chain systems for improved subcontractor performance on Leadership in Energy and Environmental Design (LEED) certified building projects. The construction management of a LEED project has an important objective of meeting sustainability certification requirements. This is in addition to the typical project management objectives of cost, time, quality, and safety for traditional projects; and, therefore increases the complexity of LEED projects. Considering that construction management organizations rely heavily on subcontractors, poor performance on complex projects such as LEED projects has been largely attributed to the unsatisfactory preparation of subcontractors. Furthermore, the extensive use of unique and non-repetitive short term contracts limits the full integration of subcontractors into construction supply chains and hinders long-term cooperation and benefits that could enhance performance on construction projects. Improved subcontractor development practices are needed to better prepare and manage subcontractors, so that complex objectives can be met or exceeded. While supplier development and supply chain theories and practices for the manufacturing sector have been extensively investigated to address similar challenges, investigations in the construction sector are not that obvious. Consequently, the objective of this research is to investigate effective subcontractor development practices and processes to guide construction management organizations in their development of a strong network of high performing subcontractors. Drawing from foundational supply chain and supplier development theories in the manufacturing sector, a mixed interpretivist and empirical methodology is utilized to assess the body of knowledge within literature for conceptual model development. A self-reporting survey with five-point Likert scale items and open-ended questions is administered to 30 construction professionals to estimate their perceptions of the effectiveness of 37 practices, classified into five subcontractor development categories. Data analysis includes descriptive statistics, weighted means, and t-tests that guide the effectiveness ranking of practices and categories. The results inform the proposed three-phased LEED subcontractor development program model which focuses on preparation, development and implementation, and monitoring. Highly ranked LEED subcontractor pre-qualification, commitment, incentives, evaluation, and feedback practices are perceived as more effective, when compared to practices requiring more direct involvement and linkages between subcontractors and construction management organizations. This is attributed to unfamiliarity, conflicting interests, lack of trust, and resource sharing challenges. With strategic modifications, the recommended practices can be extended to other non-LEED complex projects. Additional research is needed to guide the development of subcontractor development programs that strengthen direct involvement between construction management organizations and their network of high performing subcontractors. Insights from this present research strengthen theoretical foundations to support future research towards more integrated construction supply chains. In the long-term, this would lead to increased performance, profits and client satisfaction.

Keywords: construction management, general contractor, supply chain, sustainable construction

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Authors: Adamu Mudi, Ibrahim Wahab Fawole, Abubakar Abba Kolo

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In this research paper, we present a microcontroller-based liquid level controller that identifies the various levels of a liquid, carries out certain actions, and is capable of communicating with the human being and other devices through the GSM network. This project is useful in ensuring that a liquid is not wasted. It also contributes to the internet of things paradigm, which is the future of the internet. The method used in this work includes designing the circuit and simulating it. The circuit is then implemented on a solderless breadboard, after which it is implemented on a strip board. A C++ computer program is developed and uploaded into the microcontroller. This program instructs the microcontroller on how to carry out its actions. In other to determine levels of the liquid, an ultrasonic wave is sent to the surface of the liquid similar to radar or the method for detecting the level of sea bed. Message is sent to the phone of the user similar to the way computers send messages to phones of GSM users. It is concluded that the routine of observing the levels of a liquid in a tank, refilling the tank when the liquid level is too low can be entirely handled by a programmable device without wastage of the liquid or bothering a human being with such tasks.

Keywords: Arduino Uno, HC-SR04 ultrasonic sensor, internet of things, IoT, SIM900 GSM module

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Authors: Sonal H. Singh, Bhaskar Bhowmick

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The research hypothesis formulated in this paper examines the importance of microfinance provision for entrepreneurship development by engendering a high level of entrepreneurial orientation among the grassroots entrepreneurs. A theoretically well supported empirical framework is proposed to identify the influence of financial services and non-financial services provided by microfinance institutes in strengthening the impact of grassroots innovation on entrepreneurial orientation under resource constraints. In this paper, Grassroots innovation is perceived in three dimensions: new learning practice, localized solution, and network development. The study analyzes the moderating effect of microfinance provision on the relationship between grassroots innovation and entrepreneurial orientation. The paper employed structural equation modelling on 400 data entries from the grassroots entrepreneurs in India. The research intends to help policymakers, entrepreneurs and microfinance providers to promote the innovative design of microfinance services for the well-being of grassroots entrepreneurs and to foster sustainable entrepreneurship development.

Keywords: entrepreneurship development, grassroots innovation, India, structural equation model

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Authors: Somayyeh Naeemi, Ezhar Tamam

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In most of the countries, Facebook allocated high rank of usage among other social network sites. Several studies have examined the effect of Facebook intensity on individuals’ psychological well-being. However, few studies have investigated its effect on eudaimonic well-being. The current study explored how emotional dependency to Facebook relates to psychological well-being in terms of eudaimonic well-being. The number of 402 adolescents 13-16 years old who studied in upper secondary school in Malaysia participated in this study. It was expected to find out a negative association between emotional dependency to Facebook and time spent on Facebook and psychological well-being. It also was examined the moderation effects of self-efficacy on psychological well-being. The results by Structural Equation Modeling revealed that emotional dependency to Facebook has a negative effect on adolescents’ psychological well-being. Surprisingly self-efficacy did not have moderation effect on the relationship between emotional dependency to Facebook and psychological well-being. Lastly, the emotional dependency to Facebook and not the time spent on Facebook lessen adolescents’ psychological well-being, suggesting the value of investigating Facebook usage among college students in future studies.

Keywords: emotional dependency to facebook, psychological well-being, eudaimonic well-being, self-efficacy, adolescent

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Authors: Pankaj Negi

Abstract:

Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power. Custom power instruments are of the most well-known compensators of power quality in distributed network. This paper present a comprehensive review of compensating custom power devices mainly DSTATCOM (distribution static compensator),DVR (dynamic voltage restorer), and UPQC (unified power quality compensator) and also deals with sliding mode control and its applications to custom power devices. The sliding mode control strategy provides robustness to custom power device and enhances the dynamic response for compensating voltage sag, swell, voltage flicker, and voltage harmonics. The aim of this paper is to provide a broad perspective on the status of compensating devices in electric power distribution system and sliding mode control strategies to researchers and application engineers who are dealing with power quality and stability issues.

Keywords: active power filters(APF), custom power device(CPD), DSTATCOM, DVR, UPQC, sliding mode control (SMC), power quality

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Authors: Harshith Gowda K. S, Tejaskumar N, Shubhanga R. B, Gowtham N, Deekshith Gowda H. S

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Phasor measurement units (PMU) are playing an important role in the current power system for state estimation. It is necessary to have complete observability of the power system while minimizing the cost. For this purpose, the optimal location of the phasor measurement units in the power system is essential. In a bus system, zero injection buses need to be evaluated to minimize the number of PMUs. In this paper, the optimization problem is formulated using mixed integer programming to obtain the optimal location of the PMUs with increased observability. The formulation consists of with and without zero injection bus as constraints. The formulated problem is simulated using a CPLEX solver in the GAMS software package. The proposed method is tested on IEEE 30, IEEE 39, IEEE 57, and IEEE 118 bus systems. The results obtained show that the number of PMUs required is minimal with increased observability.

Keywords: PMU, observability, mixed integer programming (MIP), zero injection buses (ZIB)

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Authors: Amhimmid Mohammed Saffour, Mustafa Mohamed Abdullah

Abstract:

The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable.

Keywords: medical image, Matlab, image compression, wavelet's, objective evaluation

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: Vuk M. Popovic, Dunja D. Popovic

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Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs

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Authors: Ashiraliyev SHavkat

Abstract:

Relevance of the topic: Diabetes mellitus in overweight people development and absence of treatment measures. Objective: to give patients the correct instructions on proper nutrition, to organize a network of preventive and therapeutic measures. Materials and methods: Multidisciplinary Tashkent Medical Academy. As a result of objective observations in patients who applied to the clinic, 28 11 overweight patients had to type 2 diabetes. Diabetesmellituswasdiagnosed. Results: 11.5 mmol / L on an empty stomach in the morning. EDT yes. Pathogenesis: fat content in the diet of patients with diabetes mellitus. Carbohydrate foods make up 60%. Eating disorders and physical inactivity As a result, the accumulation of glucose in the form of fat increases, and this is constantly in the blood, which led to an increase in the number of fatty acids. Clinic: Frequent fasting in 11 patients (hypothalamus). Associated with glucose deficiency), drinking 8-9 liters of water per day of blood in 7 people Systolic pressure 150 diastolic pressures 100. Sensation of ants in 3 people and poor eyesight in 5 people. Conclusion: Explain to patients that nutritional guidelines should be followed. Assign active movement in accordance with the energy entering the body.

Keywords: mellitus, diabetes, pathogenesis, clinic

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Authors: Mobarok Hossain Bhuyain

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Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

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Authors: Nesrine Bougarech, Habib Affes

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This research lays the emphasis on the business process reengineering (BPR) which consists in radically altering the organizational processes through the optimal use of information technology (IT) to attain major enhancements in terms of quality, performance and productivity. A survey of the business process reengineering (BPR) was carried out in three French groups and their subsidiaries in Tunisia. The data collected were qualitatively analyzed in an attempt to test the main indicators of the success of a business process reengineering project (BPR) and to compare the importance of these indicators in the context of France versus Tunisia. The study corroborates that the respect of the inherent principles of the business process reengineering (BPR) and the diversity of the human resources involved in the project can lead to better productivity, higher quality of the goods or services and lower cost. Additionally, our results mirror the extent to which the respect of the principles and the diversity of resources are more important in the French companies than in their Tunisian subsidiaries.

Keywords: business process reengineering (BPR), human resources management (HRM), information technology (IT), management

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Authors: Abdessalam Hijab, Hafida Boulekbache, Eric Henry

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The objective of this article is to explain the use of geographic information system (GIS) and information and communication technologies (ICTs) in the real-time processing and analysis of data on the status of an urban sanitation network by integrating professional actors in sanitation for sustainable management in urban areas. Indeed, it is a smart geo-collaboration based on the complementarity of ICTs and GIS. This multi-actor reflection was built with the objective of contributing to the development of complementary solutions to the existing technologies to better protect the urban environment, with the help of a checklist with the spatial reference "E-Géo-LD" dedicated to the "professional/professional" actors in sanitation, for intelligent monitoring of liquid sanitation networks in urban areas. In addition, this research provides a good understanding and assimilation of liquid sanitation schemes in the "Lamkansa" sampling area of the city of Casablanca, and spatially evaluates these schemes. Downstream, it represents a guide to assess the environmental impacts of the liquid sanitation scheme.

Keywords: ICT, GIS, spatial checklist, liquid sanitation, environment

Procedia PDF Downloads 236

Authors: Marilyn U. Balagtas, Maria Ruth M. Regalado, Carmelina E. Barrera, Ramer V. Oxiño, Rosarito T. Suatengco, Josephine E. Tondo

Abstract:

This study presents the perceptions of the students and teachers from kindergarten to tertiary level of the image of the 21st century teacher to provide basis in designing teacher development programs in Teacher Education Institutions (TEIs) in the Philippines. The highlights of the report are the personal, psychosocial, and professional images of the 21st century teacher in basic education and the teacher educators based on a survey done to 612 internal stakeholders of nine member institutions of the National Network of Normal Schools (3NS). Data were obtained through the use of a validated researcher-made instrument which allowed generation of both quantitative and qualitative descriptions of the teacher image. Through the use of descriptive statistics, the common images of the teacher were drawn, which were validated and enriched by the information drawn from the qualitative data. The study recommends a repertoire of teacher development programs to create the good image of the 21st century teachers for a better Philippines.

Keywords: teacher image, 21st century teacher, teacher education, development program

Procedia PDF Downloads 367

Authors: Ma Lei-Lei, Zhou You

Abstract:

Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

Procedia PDF Downloads 98

Authors: Abhishek Kumar Chandra, Kaushal Kishor, Wasim Khan, Dhananjay Singh, M. S. Alam

Abstract:

Single phase fluid flow through series of uniform microchannels connected via transition section (converging-diverging section with or without throat) was analytically and numerically studied to characterize the flow within the channel and in the transition sections. Three sets of microchannels of diameters 100, 184, and 249 μm were considered for investigation. Each set contains 10 numbers of microchannels of length 20 mm, connected to each other in series via transition sections. Transition section consists of either converging-diverging section with throat or without throat. The effect of non-uniformity in microchannels on pressure drop was determined by passing water/air through the set of channels for Reynolds number 50 to 1000. Compressibility and rarefaction effects in transition sections were also tested analytically and numerically for air flow. The analytical and numerical results show that these configurations can be used in enhancement of transport processes. However, converging-diverging section without throat shows superior performance over with throat configuration.

Keywords: contraction-expansion flow, integrated microchannel, microchannel network, single phase flow

Procedia PDF Downloads 280

Authors: Sam Solaimani, Jack A. A. van der Veen, Mehdi Latifi

Abstract:

Considering the increasingly VUCA (Volatile, Uncertain, Complex, Ambiguous) business market, innovation is the name of the game in contemporary business. Innovation is not solely created within the organization itself; its 'network environment' appears to be equally important for innovation. There are, at least, two streams of literature that emphasize the idea of using the extended organization to foster innovation capability, namely, Supply Chain Collaboration (SCC) (also rooted in the Lean philosophy) and Open Innovation (OI). Remarkably, these two concepts are still considered as being totally different in the sense that these appear in different streams of literature and applying different concepts in pursuing the same purposes. This paper explores the commonalities between the two concepts in order to conceptually further our understanding of how OI can effectively be applied in Supply Chain networks. Drawing on available literature in OI, SCC and Lean, the paper concludes with five principles that help firms to contextualize the implementation of OI to the peculiar setting of SC. Theoretically, the present paper aims at contributing to the relatively under-researched theme of Supply Chain Innovation. More in practical terms, the paper provides OI and SCC communities with a workable know-how to seize on and sustain OI initiatives.

Keywords: lean philosophy, open innovation, supply chain collaboration, supply chain management

Procedia PDF Downloads 323