International Journal of Engineering and Manufacturing @ijem
Статьи журнала - International Journal of Engineering and Manufacturing
Все статьи: 571
Alternative device for non-ionizing radiation detection
Статья научная
Detection of non-ionizing electromagnetic radiation is central to managing health and environmental hazards resulting from its exposure. This research focused on the design and development of a non-ionizing electromagnetic radiation detector that is sensitive to the medium frequency of 50Hz to 30MHz and their corresponding power density. The device consists of the sensing, amplifying, filtering and microcontroller sections. The sensing section is made up of a coil wound on a ferrite rod, it detects radiations from the surroundings and converts it to a voltage signal. The voltage produced is then fed to the operational amplifiers in the amplifying section. Afterwards, the output signal is fed to the filtering section where unwanted signals are eliminated. The analogue signal output from the active filter is then fed to the microcontroller section where it is converted to a digital signal through the analogue to digital converter (ADC). The ADC then presents the converted signal in a readable form to be displayed on the liquid crystal display (LCD). The developed equipment was calibrated (in µW/cm2) using an existing detector EMF DT 1130. With an average calibration coefficient value of 2.32, the detector was found to perform excellently well at both medium and low-frequency ranges.
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An Adaptive User Authentication Architecture for Drunk Driving and Vehicle Theft Mitigation
Статья научная
The high rate of vehicle theft and the loss of lives occasioned by drunk driving has caused irreparable losses to people and businesses, from a personal, commercial and reputation perspective. Existing systems deployed to mitigate against vehicle theft have all been breached by the ever-adaptive criminals. Drunk driving has been estimated to be a leading cause of deaths on highways and motorways, through preventable accidents. Technology has provided the tools that can aid in mitigating the vices aforementioned with the aim of provisioning lasting solutions. This paper proposes a new architecture for adaptive user authentication in order to mitigate drunk driving and vehicle theft. It considered user authentication in three (3) phases and proposed an authentication architecture for each identified phase, with a step by step description of the implementation method and tools for each phase. The architecture proposed in this study can aid in real time prevention of vehicular theft, unauthorized vehicular access and usage, while also utilizing the benefits of the latest technologies in machine vision and alcohol breadth analyzers to detect and prevent drunk driving, and the associated accidents it causes.
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Статья научная
In this paper we propose a low-rank alternating direction implicit (ADI) method to solve projected generalized continuous-time Sylvester equations with low-rank right-hand sides. Such equations arise in control theory including the computation of inner products and norms, and the model reduction based on balanced truncation for descriptor systems. The requirements of this method are moderate with respect to both computational cost and memory. Numerical experiments presented in this paper show the effectiveness of the proposed method.
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An Analysis on Price Dispersion in Online Retail Market Based on the Different of the Product Levels
Статья научная
This paper cares about the online price dispersion and analyses the online retail commodity as a complex. This paper analyses the attributes of the product layer of the online retail commodity and the characters of the online customers, and divided the online payment into two parts: the payment of the core value and the willingness of the added-value payment. And then this paper explains the online price dispersion by the attributes of online retail commodity’s dimension which affect the online customers directly.
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An Application of Rule-Based Classification with Fuzzy Logic to Image Subtraction
Статья научная
Surveillance camera is used as a new technology for security. In this research, the combination of OpenCV with image processing will be discussed. Saving the space in the hard drive by recording only video when here would be an image formed in the subtraction of the original image to the next image captured. With the use of Image Processing and Fuzzy logic, the research was enhanced by eliminating the recording of same image captured. After analyzing the background images, it can now determine when to start recording the video or when to stop recording a video by subtracting the images in the backdrop image and comparing the image if there was an object in motion using template matching. With the application of the project, memory storage saved up to forty-six percentage points.
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Статья научная
Researches on the relation of a loaded mechanical energy to the damaged banana maturation and its quality evolution has significance to its storage, but there are few researches at present. This paper studied on the effect of the loaded energy on banana to its internal maturation substance evolution and stored quality with time, including the change of maximum impact stress, re-enduring impact energy. The results showed there existed a significant correlation between energy transformation and increment of maturity substance in the damaged banana. Also, there existed a significant association between energy transformation and increment of maturity substance in the damaged banana in its storage time, and appeared a trends that the mechanical energy being transferred into fruit internal energy increased its maturity substance and decreased its stored quality, but individual maturity difference had less effect. Thus, decreasing the energy and the substance transformation in damage area are the important means of extending the physiology life of fruits, which is an urgent problem to be solved in their field of processing and storage at present.
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An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model
Статья научная
A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.
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Статья научная
This study explores the conversion of English to Hindi, first to text, and subsequently to speech. The first part of the implementation is the text recognition from images, in which two approaches are used for text character recognition: a maximally stable extensible region (MSER) and grayscale conversion the second part of the paper deals with the geometric filtering in combination with stroke width transform (SWT). Subsequently, letter/alphabets are grouped to detect text sequences, which are then fragmented into words. Finally, a 96 percent accurate spell check is performed using naive Bayes and decision tree algorithms, followed by the use of optical character recognition (OCR) to digitize. The word Give our text-to-speech synthesizer (TTS) the recognized text to convert it to Hindi language using the text-to-speech model. Based on aspects such speech speed, sound quality, pronunciation, and clarity.
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An Efficient Genetic Algorithm Orienting to the Protein Fold Prediction
Статья научная
Proteins are amino acid chains that acquire their biological and biochemical properties by folding into unique 3-dimensional structures. The biological function of a protein is dependent on the protein folding into the correct, or "native", state. At present, there are so many ideas to predict the structure of the protein folding. This paper first present the concept of protein folding and how is significant to study protein fold prediction. In this paper we join the simulated annealing factor into Parallel Genetic Algorithm and use this hybrid Parallel GA to predict the structure of protein fold. The revised algorithm is more efficient than traditional Genetic Algorithm and simulated annealing algorithm.
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An Empirical Study on Consumers’ Continuance Intention Model of Online Group-buying
Статья научная
This paper examines the factors influencing consumers’ intention to continue using a new pattern of online group-buying, which benefits consumers from high discounts and is prevalent in many countries of the world since 2009. A theoretical model is proposed based on Expectation-Confirmation Model of IS Continuance (ECM-ISC) from the previous IS continuance literature, integrating with e-recovery service quality, perceived risk and two external variables. Data collected from a questionnaire survey of OGB consumers provides empirical support for the proposed model. The results indicate that consumers’ satisfaction with prior use and perceived usefulness significantly influence consumers’ continuance intention. Consumers’ satisfaction is determined primarily by consumers' confirmation of expectation from prior use and secondarily by perceived e-recovery service quality. Further, confirmation also has a significant influence on post-acceptance perceived usefulness. Confirmation, in turn, is significant influenced by both product quality and information quality. Some suggestions for practitioners are also offered.
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An Improved DWT-SVD Based Robust Digital Image Watermarking for Color Image
Статья научная
The Digital Watermarking Technique has gained its importance due to its ability to provide the secure mechanism for copyright protection and authenticity of the digital data in this high growing internet and computer technology where the tampering and distribution of digital data illegally from unauthorized users is inevitable. For these two important properties of Digital Watermarking, i.e. Robustness and Imperceptibility of watermarked image must take into consideration. In this paper, invisible robust digital watermarking is proposed using Discrete Wavelet Transform and Singular Value Decomposition in YCbCr Color space. The performance of the proposed algorithm is compared with some previous works and results found are more robust against various geometric attacks.
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An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System
Статья научная
Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT - 20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.
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An Improved HARQ Mapping Mechanism in LTE-Advanced System
Статья научная
Based on the analysis of the current hybrid automatic repeat request (HARQ) mapping mechanism and the corresponding problems in LTE-Advanced system with carrier aggregation, this paper designs an improved HARQ mapping mechanism, in which an idea of semi-static mapping is introduced. The simulation results validate its effectiveness in improving system performance through the trade-off between diversity gain and overhead.
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Статья научная
Virtualization technology play a vital role in cloud computing. In virtualization environment multiple applications can run at a same time. VM migration is one of the important features of the virtualization, which allows application to be transparently migrated along with their execution environments across physical machines. VM migration consists of four steps. These steps are source PM selection, VM selection from the selected PM in first step, target PM selection for placing the selected VM and selecting method for transfer VM data. In our proposed approach we are focusing on the last step i.e., choosing method for transferring VM data. In this paper proposed approach is divided into two phases. First phase filter all pages which is modified in the last iteration and unmodified in the current iteration. In the second phase, page is divided into two types i.e., high dirty page and normal based on the modification in the last few iterations. For all filtered pages in the first phase now we check the number of times when the page is modified from the history record. If the page is modified more times the page will not be send in the current iteration otherwise the page will be send to the destination VM. To evaluate the performance of the proposed approach it is implemented in CloudSim simulator and compare with the existing time series based pre-copy approach in term of total migration time and down time. Experiment result shows that proposed approach gives better result as compare to the base approach.
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An Innovative Leukemia Detection System using Blood Samples via a Microscopic Accessory
Статья научная
Leukemic patients are in a rapid increase. Hence, the use of microscopic images of blood samples through visual inspection to identify blood disorders has increased, opening the door for computerized techniques for detecting leukemia. This project applies computer vision techniques to increase the accuracy and speed of detection from periph-eral blood. It also enhances visualization by providing an appropriate supplement to traditional microscopy. A micro-computer (Raspberry Pi) was well programmed in Python for analyzing images with the help of a Raspberry Pi camera and a touch screen as an alternative to the eyepiece. To achieve diversity and seek for more accuracy, image datasets for this project were obtained from various resources. These datasets were then analyzed through image processing techniques to detect leukemia cells. This detection process involves resizing cells to a standard size, noise removal by linear scaling filter, global-local contrast enhancement, segmentation of white blood cells (WBCs) using marker-controlled watershed algorithm, overlapping detection and separation using watershed and k-means clustering algorithms, and extraction with selection of the most relevant features from cells. These features were then imported into the Support Vector Machine (SVM) model which resulted in an accuracy of 93.2773%. A standalone desktop application with a suitable graphical user interface (GUI) was implemented. It was then uploaded into the Raspberry Pi, some code lines were rewritten for dealing with the camera, the hardware was designed and implemented, and then formal experiments were conducted resulting in the detection of leukemia in 5 samples out of 6. This implies that precise detection can be implemented with different data taken in various imaging conditions.
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Статья научная
Optical Character Recognition Systems (OCR) is a tool that helps computers read text from pictures of papers. It makes it easier for machines to understand what the words say without needing a person to read it out loud. It allows for easy digitizing of historical documents, archival material, and medical records thereby saving on their retrieval times. However, the accuracy of OCR systems heavily relies on the quality of the input images. To negate the contribution of the quality of input images to the accuracy of OCR systems, in this paper, we propose an integrated image pre-processing pipeline integrated with the OCR systems that enhances the quality of input images for efficient image to text conversion. This method results in an easily understandable text output with a lower Character Error Rate (CER) in comparison to the current methods. In addition, we explore a technique for converting text from a document or image into machine-readable form and then converting it to audio output using gTTS, a Python library that interfaces with Google Translate's text-to-speech API. We assess the effectiveness of this approach and illustrate that it substantially enhances OCR precision when compared to other existing methods. This paper presents a clear overview of the growth phases and significant obstacles, accompanied by compelling comparisons of results achieved through various methods.
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Статья научная
Birth marks the evolution from a fluid environment to one in which the baby breathes. Respiration difficulties after birth and in the first hours of life are common. Some babies will experience more severe respiration problems, and some of these will require medical care that only expert physicians can offer. In the first six hours of life, asphyxia is recognized. Neonatal asphyxia is a syndrome categorized by the deprivation of oxygen supply at birth and causes many morbidities and mortalities among newborns. Improved results depend on early detection and action. Using the African Vultures Algorithm that performs hyperparameter optimization expands accuracy and effectiveness in supporting the model. This integration improves the detection and sympathetic of neonatal asphyxia by using African Vultures Algorithm's capability to find fine results in high-dimensional places as well as the capability of LGNN to method structured clinical data. Herein, our model excels over present deep learning models and computer vision techniques for accurate detection of neonatal asphyxia. LGNN model achieved an accuracy of 98% in Dataset 1 and 96.5% in Dataset 2. F1-Score of the proposed model was achieved as 97.6% in Dataset 1 and 96.7% in Dataset 2. In Dataset 1 and Dataset 2, LGNN model as long as 97.9% and 97.1% precision, individually. In Dataset 1 and Dataset 2, the proposed (LGNN) provided 98.8% and 97.7% recall, one-to-one. Efficiently exploit the graph structure for feature aggregation and inference by employing a linear graph neural network architecture. Improved newborn outcomes are made possible by the suggested model, which is a potent diagnostic tool that also helps clinicians comprehend underlying pathophysiological causes.
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Статья научная
Nonpolluting, renewable energy can be harnessed from the sun using solar thermal concentrator and photovoltaic systems. A novel asymmetric oblique compound conical solar concentrator was designed and constructed with automatic solar tracking system. The experimental study of the developed system was carried out to inspect the efficiency of the designed solar concentrator and automatic azimuth and altitude tracking. In this solar tracking, the RTC (Real Time clock) based active solar tracking method is used to track the sun in order to attain the maximum temperature. A simple microcontroller based control system is used to control this open loop solar tracking system, further the concentrated thermal power was measured using both the K-type temperature sensor and thermometer gun. The temperature obtained at the receiver of solar concentrator is sent to the measurement unit using GSM wireless module available at remote location. The monitoring unit receives the temperature values and recoded them regularly. Besides, the maximum average solar thermal power of the system was observed to be during 10.00 AM to 3.00 PM. This temperature is useful for various heating applications.
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Статья научная
CMOS is a technology that has revolutionized the field of electronics. Over the time the processing technologies and design methodologies of CMOS devices have proved to be in full swing with the Moore’s law and the miniaturization paradigm. However, after surviving for more than five decades, CMOS is now facing challenges to live through the submicron ranges. The scaling in CMOS has reached a higher limit, showing adverse effects not only from physical and technological point of view but also from material and economical perspective. This drift inspires the researchers to look for new promising alternatives to CMOS which vow better performance, density and power consumption. One of the promising alternatives to digital designing in CMOS is the Quantum-dot Cellular Automata (QCA). QCA is a technology that involves no current transfer but works on electronic interaction between the cells. The QCA cell basically consists of quantum dots separated by certain distance and the entire transmission of information occurs via the interaction between the electrons localized in these quantum dots. In this paper the limitations to CMOS in submicron range and concepts for designing in QCA have been discussed. Further the building blocks are explained theoretically as well as using QCA Designer implementations with focus on cell interaction and clocking mechanisms.
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Статья научная
Sugarcane bagasse ash (SBA) is a solid waste obtained from the boilers of sugar factories after the combustion of sugarcane bagasse. Sugarcane bagasse ash mainly consists of amorphous silica and can be used as a supplementary cementitious material in the production of concrete. In the present study, the physical properties of sugarcane bagasse ash, namely, particle size distribution, surface area, specific gravity, morphology and chemical properties like elemental composition of SBA and ordinary Portland cement (OPC) have been investigated. The microstructural observations were made using Scanning Electron Microscope (SEM). Elemental compositions were analyzed and images coupled to microanalysis by Energy dispersive spectroscopy (EDS). The microstructure observations and elemental characterization of cement concrete matrix was also carried out after 28 days of curing. Blended cement concrete specimens were prepared using SBA up to 30% in regular intervals of 10% by weight of pure Portland cement, coarse and fine aggregate, tap water and superplastisizer. SEM results indicates that substitution of SBA particles by Portland cement produces dense matrix as compared to control mix and EDS analysis shows fluctuations in calcium and silica concentrations as the cement replacement level increases in the blended cement concrete.
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