International Journal of Engineering and Manufacturing @ijem
Journal articles - International Journal of Engineering and Manufacturing
All articles: 623
A Service-oriented Architecture Framework for Emergency Vehicles Information System
Scientific article
Using Service-Oriented Architecture (SOA) to build an information system is a hot topic at present. The primary goal of this paper is to investigate how to build a SOA-based information system in a special industry-the field of emergency vehicles in environment protection industry. The paper provides a presentation of current compositions of the emergency vehicles information system and then gives out the system structure. The paper proceeds with the business process flow and working principle of an emergency vehicle information system. A service-oriented architecture framework for emergency vehicles information system is proposed.
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A Service-oriented Dual-bus BAM System Model
Scientific article
Based on a thorough analysis and comparison of several business activity monitoring(BAM) system models, noting that a BAM system is open, integrative, and real-time, and using the SOA software engineering techniques, this paper presents a service-oriented dual-bus BAM system model and analyzes in detail the functions of its service components and communications among them. Much more open than prior models, this BAM system model has a two-level bus architecture, which enables flexible and extensible implementations of practical systems, as well as provides a foundation for software reuse.
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A Simple Economics in Local Hospital Information Management
Scientific article
Can the rural people benefit from New Cooperative Medical Scheme? We set up a simple economic model to show that the rural patient can benefit from NCMS under a good supervision system which depends on the hospital information management. Based on the theoretical model, we give some suggestions for the current local hospital information management.
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A Simple Emotion Discrimination Technique Based on Triangle Phase Space Mapping of HRV Signals
Scientific article
Physiological signal processing techniques are commonly used in emotion recognition. Heart rate variability (HRV) is an important tool in disease diagnosis and psychological investigations. Because of the chaotic nature of HRV, customary methods may not be proficient. Taking the advantage of geometrically based algorithms can lead to the uncomplicated and better representation of heart rate dynamics. The aim of this study was to test whether a simple HRV measure, based on triangle phase space mapping and polynomial fitting, provides a useful emotion recognition technique. HRV of women (n = 12) aged 19-25 years were compared to that of 12 matched aged men, while subjects were induced by four emotional stimuli: happy, sad, afraid, and relax. Kruskal-Wallis test was applied to show the level of significance of the features. The results confirm that emotional responses to sad, afraid and relax stimuli can be differentiated by the proposed indices. In addition, they are significantly different in both genders' physiological reactions. It seems that the suggested simple quantifiers are most promising in offering new insight into the dynamics assessments of HRV signals in different emotional states.
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Scientific article
The hydrophobicity of peptide is an important factor that affects the dissolution behavior of proteins and peptides, also affect the physical and chemical properties. In this study, each amino acid side chain was characterized using three structure parameters (heuristic molecular lipophilicity potential, HMLP). The HMLP parameters, total surface area(S), lipophilic indices (L), and hydrophilic indices (H) of amino acid side chains are derived from theoretical computation. Based on HMLP descriptors, QSAR models of the logP were constructed for blocked and unblocked dipeptides by multiple linear regression (MLR) and support vector regression (SVR). All the results showed that the logP relates to the total surface area(S) and hydrophilic indices (H), and the prediction results of SVR are better than that of MLR. The prediction results are in agreement with the experimental values. The result shows HMLP parameters (S,L,H) could preferably describe the structure features of the peptides responsible for their octanol to water partition behavior.
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A Smart Bag for School Students Safety and Security in Oman
Scientific article
The idea behind the paper is to transform the conventional school bag into a smart bag connected to the Internet of Things and aimed at elementary school pupils. Its concept uses GPS to follow the student's location; whenever it detects dangers like gas and smoke around the student, it sends a signal to the user. By lessening the weight on the student with the use of the load sensor, it can also determine the true weight of a bag. It can also be utilized on school buses in case a student is overlooked by notifying the driver of their presence via an LCD on the vehicle that is connected to the gas sensor. The results obtained have shown that the proposed research work successfully developed a prototype that is able to provide security and safety by delivering messages to the user, determining the actual weight of the bag, and tracking the student's location.
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A Solution for Monitoring Temperature and Humidity at 31 Explosive Materials Company
Scientific article
Assuring product safety and quality in the explosives manufacturing process is critical today to protect worker and environmental safety. Temperature and humidity in the manufacturing plant are critical factors to consider because they can impact the manufacturing process and the quality of the final product. In this work, we design a temperature and humidity monitoring system for 31 explosive materials company using ethernet communication standard. In explosives factories, this communication standard is more suitable than other commonly used wireless communication technologies. We tested the system at 31 explosive materials factory. Test results show that the system operates stably and accurately. This system assists factory operators in increasing production efficiency, reducing dangers, and ensuring the quality of explosives.
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A Study on Ancient Temple Structural Elements Recognition Using Genetic Algorithm
Scientific article
The systematic and scientific study of the lifestyle and culture of earlier peoples is known as archaeology. The Indian history of archaeology spans from the 19th century to the present status, it includes the region's history investigated by a wide variety of archaeologists. They don’t have any such authentic digital methods to be quoted in research. Manual Recognition of ancient temple structural elements is quite difficult to recognize, has archaeologists face many complex problems because they don’t have any automated Recognition method. Recognition is helpful for archaeologists to get more detailed information of ancient temples which is very important for further research. Thus, in this work it is proposed to develop automated method for Recognition ancient temple structural elements (vimana & pillars) for further archaeological research purpose. The proposed method extracts Genetic programming evolved spatial descriptor and classifies the temple structural elements visited by archaeologists based on linear discriminant method [LDA]. The proposed method is divided into 3 main phases: pre-processing, genetic programming evolution and Recognition. The Generalized Co-Occurrence Matrix is used in the pre-processing phase to change images into a format that genetic programming systems may use to process them. The second stage produces the best spatial descriptor to date in the form of a programme that is based on fitness Utilizing LDA, the Fitness is determined. Once the program's output has been received, it can be used for Recognition. Experimental results shows, it demonstrates relatively high accuracy in Recognizing both vimana(gopura) & pillars of different temples. The proposed method is implemented in MATLAB. These results will play very significant role in identification of temple architecture, which in-turn helps in conservation and reconstruction of temples. The proposed methodology will give 98.8% accuracy in pillars recognition and 98.4% accuracy in vimana recognition.
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A Study on Sustainable Development of Grain Production Coping with Regional Drought in China
Scientific article
The sustainable development of grain production is a necessary condition for Chinese rapid economic development, which is directly related to people's livelihood and national security. This paper analyzed the frequent occurrence causes of regional drought in China, emphatically enumerated the North-South imbalance of precipitation distribution in time and space, the excessive tillage and grazing and vegetation damage, the hydro-agricultural infrastructure aging and the rapid urbanization diverted for agricultural water resources, which impact the sustainable development of grain production, then proposed some countermeasures coping with frequently regional drought crisis: strengthen the protection of forest vegetation, the scientific planning of planting structure, and the balance regulation of urban industrial water and grain irrigation water, surface water and ground water and of the water allocation between the administrative region, and solve the discharge of sewage purification and desalination technologies, thus establish an integrated balance system of total supply and regional control of grain irrigation water, so as to continuously perfect the durable and stable supply of grain in China.
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A Survey of Data Mining Techniques for Indoor Localization
Scientific article
The important need for suitable indoor positioning systems has recently seen an exponential rise with location-based services emerging in many sectors of human life. This has led to adopting techniques to mine location data to discover useful insights to improve the accuracy of the various indoor positioning systems. Although indoor positioning has been reviewed in some literary works, an in-depth survey of how data mining could improve the performance of indoor localization systems is still lacking. This paper surveys data mining techniques such as Na¨ıve Bayes, Regression, K-Means, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Expectation Maximization (EM), Neural Networks (NN), and Deep Learning (DL) including how they were used to improve the accuracy of indoor positing systems using various supporting technologies such as WiFi, Bluetooth, Radio Frequency Identification (RFID), Visible Light Communication (VLC), and indoor localization techniques such as Received Signal Strength Index (RSSI), Channel State Information (CSI), fingerprinting, and Time of Flight (ToF). Additionally, we present some of the challenges of existing indoor positioning systems that employ data mining while highlighting areas of future research that could be exploited in addressing those challenges.
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A Survey on Facial Expression Recognition Technology and Its Use in Virtual Systems
Scientific article
Human Facial expressions are the most expressive way to display their non verbal emotions. Human can easily detect and interpret faces to understand the emotions of the person in front of them. An important aspect of facial expression recognition is its implementation in the virtual domain. Different apps for auto-face recognition can be an important factor for the growth of components of natural human-machine interfaces. Many attempts are made from few years for the development of automated systems for which will detect human face expressions and in turn their moods. This paper gives a survey over the expertise used for human moods detection and recognition of facial expression recently.
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A Survey on Stereo Matching Techniques for 3D Vision in Image Processing
Scientific article
Extraction of three-dimensional scene from the stereo images is the most effective research area in the field of computer vision. Stereo vision constructs the actual three-dimensional scene from two stereo images having different viewpoints. Stereo matching is a correspondence problem, that means it ascertains which part of image corresponds to which part of another image ,where variations inside two images is due to the movement of camera or elapse of time. Many stereo matching algorithms have been developed in order to construct the accurate disparity map. This paper presents a review on various stereo matching techniques. The comparison among existing techniques has clearly shown that none perform optimistically every time. This review has shown that the existing methods in stereo matching involve median filtering. But median filter is not effective for high density of noise. Besides mean-shift segmentation is being used for disparity refinement in existing methods, which can be enhanced by using improved mean-shift segmentation, available now days. In addition, guided filter has been used by many algorithms, but this can be replaced by joint trilateral filters.
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A Systematic Approach for Heart Disease Analysis using Machine Learning Algorithms
Scientific article
The number of heart disease patients has significantly increased in recent years, and heart illness is linked to a high death rate. Furthermore, as technology advanced, several sophisticated devices were created to assist patients in measuring their health at home and estimating their risk of developing heart disease. Using six machine learning models, the study seeks to determine how accurate self-measured physical health indicators are at predicting heart disease when compared to all indicators assessed by medical professionals. Logistics Regression, K Nearest Neighbors, Support Vector Model, Decision Tree, Random Forest, and Gradient Boosting Classifier were among the six models employed to forecast heart disease. Twelve different test findings and 1189 patients' heart disease risks are included in the database utilized for the study. While the metrics contains six outcomes that could be tested, the all metrics contained all twelve test results. The accuracy score and false negative rate were calculated for each of the five models that were built for the all metrics.The findings demonstrated that in all five models, all metrics had greater accuracy scores than existing metrics. For five machine learning models, all metrics had false negative rates that were either lower or equivalent to that of existing metrics. The results showed that all physical indicators were more accurate in predicting patients' risk of heart disease than metrics measured physical health indicators. Therefore, all physical health indicators are preferable for assessing the risk for cardiac illnesses in the absence of future development of indicators.
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A new approach for RFID tag data reading in FPGA by using UART and FIFO
Scientific article
Nowadays, Radio Frequency Identification (RFID) technology used for automatic object tracking and identifying purpose. RFID technology used in so many applications especially in automobiles, security and health care systems. RFID reader and RFID tags are main processing units in RFID system. RFID tags give the information on what an object is, where it is and even its condition, and what is happening, share related data and respond to the reader. RFID reader module used for reading data from lots of RFID tags. In convention methods [1][2] doesn’t contain any read controller methodology and doesn’t contain any storage element for RFID tags data storing. In this paper, we propose a new approach for RFID tag data reading in FPGA. This approach is more flexible in structure and easily updates the design for any applications. This design is easily adaptable for different chips and communication mechanism. This design has reading controlling methodology and along with storing capability of RFID tags by using FIFO. Here we are using Universal Asynchronous Receiver and Transmitter protocol for communicating RFID reader to PC. For simulation, we are using ModelSim software and for synthesis, purpose using XILINX ISE 14.7.
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A soft computing model of soft biometric traits for gender and ethnicity classification
Scientific article
There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint. Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image. In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively. Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity. Result of this work indicated over 96% accuracy in predicting person’s ethnicity and gender with an average recognition time of less than 2secs.
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A study in Tabu Search Algorithm to Solve a Special Vehicle Routing Problem
Scientific article
In this paper, a kind of special vehicle routing problem based on reality-- vehicle routing problem with facultative demands is presented. The attributes of the problem and the optimization target are described. The mathematical model of the problem is set up. To solve the problem, A meta-heuristic approach called tobu search (TS) is put forward. The neighborhood structure and the parameters of TS algorithm are designed respectively. The proposed algorithm is successfully applied to a case and the result indicates the TS algorithm is practicable and valid.
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ACO-QL: Enhancing ACO Algorithm for Routing in MANETs Using Reinforcement Learning
Scientific article
ACO-based routing protocols like AntHocNet have emerged as a solution for adaptive routing in MANETS. Likewise, deep Q-learning based protocols are suitable for complex and dynamic environments like MANETs and utilizing real time data for better decision-making. However, there is lack of studies in enhancing ACO-based protocols using Q-learning in a new hybrid protocol, and comparing it with the established ACO-based protocol AntHocNet. By combining ACO’s strengths (eg. Multi-agent pathfinding and historical data creatd by pheromones) and combine it with key components of Q-learning, then we have a promising protocol ready to be compared with AntHocNet. Previous studies have explored integrating ML with MANET routing, but few of them, if any, have explored enhancing ACO using ML techniques. Therefore, we propose two new protocols: ACO-QL and ACO-DQN. One uses Q-learning and the latter uses deep Q-learning. After conducting many experiments by running implementations of ACO-DQN, ACO-QL, and AntHocNet on a MANET simulation, we found out that AntHocNet is superior to ACO-DQN in terms of execution time, end-to-end delay, and path cost in most cases, but on the other hand ACO-DQN achieved better packet delivery ratio and throughput results. Meanwhile, ACO-QL consistently achieved lower packet delivery ratios than AntHocNet, and mostly matching AntHocNet’s performance in terms and of other metrics, making it a valid lightweight and faster alternative.
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AI-Based Smart Prediction of Liquid Flow System Using Machine Learning Approach
Scientific article
Predicting the liquid flow rate in the process industry has proved to be a critical problem to solve. To develop a mathematical, in-depth of physics-based prognostics understanding is often required. However, in a complex process control system, sometimes proper knowledge of system behaviour is unavailable, in such cases, the complement model-based prognostics transform into a smart process control system with the help of Artificial Intelligence. In previous research a number of prognostic methods, based on classical intelligence techniques, such as artificial neural networks (ANNs), Fuzzy logic controller, Adaptive Fuzzy inference system (ANFIS) etc., utilized in a liquid flow process model to predict the effectiveness. Due to system complexity, Computational time &over fitting the performance of the AI has been limited. In this work we proposed three machine learning regression model: Random Forest (RF), decision Tree (DT) & linear Regression (LR) to predict the flow rate of a process control system. The effectiveness of the model is evaluated in terms of training time, RMSE, MAE & accuracy. Overall, this study suggested that the Decision Tree outperformed than other two models RF & LR by achieving the maximum accuracy, least RMSE & Computational time is 98.6%, 0.0859 & 0.115 Seconds respectively.
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Absorption, Diffraction and Free Space Path Losses Modeling for the Terahertz Band
Scientific article
With the explosive increase in the data traffic of wireless communication systems and the scarcity of spectrum, terahertz (THz) frequency band is predicted as a hopeful contender to shore up ultra- broadband for the forthcoming beyond fifth generation (5G) communication system. THz frequency band is a bridge between millimeter wave (mmWave) and optical frequency bands. The contribution of this paper is to carry out an in-depth study of the THz channel impairments using mathematical models to evaluate the requirements for designing indoor THz communication systems at 300GHz. Atmospheric absorption loss, diffraction loss and free space path loss were investigated and modeled. Finally, we discuss several potential application scenarios of THz and the essential technical challenges that will be encountered in the future THz communications. Finally, the article finds that propagating in the THz spectrum is strongly dependent on antenna gain.
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Achieving Performability and Reliability of Data Storage in the Internet of Things
Scientific article
Internet of things (IoT) includes a lot of key technologies; In this emerging field, wireless sensors have a key role to play in sensing and collecting measures on the surrounding environment. In the deployment of large-scale observation systems in remote areas, when there is not a permanent connection with the Internet, the network requires distributed storage techniques for increasing the amount of data storage which decreases the probability of data loss. Unlike conventional networked data storage, distributed storage is constrained by the limited resources of the sensors. In this research, we present a distributed data storage method with the combined K-means and PSO clustering mechanism organized with the binary decision tree C4.5 in the IoT area with considering efficiency and reliability approach. This scheme can provide reliability in responding to inquiries while minimizing the use of energy and computational resources. Simulation results and evaluations show that the proposed approach, due to the distributed data storage with minimal repeat publishing according to the decision tree structure, increases the reliability and availability, reduces the communication costs, and improves the Energy consumption, saving memory consumption without registering the same event and compared to other methods performed in this area have good results.
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