Showing papers in "Journal of Electronic Imaging in 2020"
TL;DR: The proposed research work is aimed to develop a novel information processing system in IoT platform through a reliable health care monitoring system through the effective utilization of big data in IoT environment through the proposed architecture to attain minimum delay in a real time environment.
Abstract: The recent technology developments and innovations improves the life style of people through smart applications, sensors, wireless communication networks, etc., for all those technologies internet is the backbone and the information processing like accessing, distributing the necessary information is achieved through Internet of Things (IoT). IoT supports multi-disciplinary applications as an active entity in engineering, science and business discipline. Based on the user preference these applications and its services could be framed in IoT. On contrary to the development, IoT has flaws in information processing as huge volume of data is need to be handled in a single environment. Considering these facts, the proposed research work is aimed to develop a novel information processing system in IoT platform through a reliable health care monitoring system. The effective utilization of big data in IoT environment is analysed through the proposed architecture to attain minimum delay in a real time environment. Conventional models are used to compare the performance of proposed design and the experimentation is performed to verify the superior performance of proposed approach using accuracy, cost functions in terms of transmission and storage, f-measure, sensitivity and specificity.
43 citations
TL;DR: The proposed work is designed to control certain applications which are remotely placed from the control station and utilizes internet medium and Blynk server for the specific operations.
Abstract: Electrical device monitoring is an essential work for improving the efficiency of electrical energy. The industrial device monitoring process are majorly contributed with physical verification of the process going on with the electrical instrument. In some cases the monitoring work is handled with help of automated sensor controllers. The automated sensor controllers are widely used for emergency cases of the ongoing process by the electrical instruments. The system status will be displayed on a screen when the system is fully controlled by an intelligent controller. From the status certain process and equipment are able to manage by physical switches by a human operator. The proposed work is designed to control certain applications which are remotely placed from the control station. The design utilizes internet medium and Blynk server for the specific operations. A sensor based monitoring station is kept near to the electrical device for sending the status of the application system. By using this design any system can be monitored remotely without physical verification. This improves the efficiency of energy utilization by the control devices.
32 citations
TL;DR: The experimental results showed that the proposed leaf disease grade identification method based on a convolutional neural network was feasible and effective for the classification of leaf disease grades.
Abstract: To achieve high yield of crops and to avoid pesticide abuse, different control methods have been adopted according to different degrees of disease in crop plants. To address the issue, a leaf disease grade identification method based on a convolutional neural network (CNN) was proposed. First, nonuniform illumination images were processed using an adaptive adjustment algorithm based on a two-dimensional (2-D) gamma function. Then a threshold segmentation method was used to segment diseased leaf images and thus to obtain binary images. Next, the ratio of the number of pixels in the lesion area to that in the diseased leaf area was calculated. This ratio was regarded as the classification threshold of the disease grades, and therefore was used to determine the disease grade category. In addition, use of a ResNet50-based CNN was proposed to identify disease grades, with a focal loss function replacing the standard cross entropy loss function, and with the Adam optimization method. Finally, leaf disease grade identification was performed on a database containing 10 types of disease leaf images for 8 crops, and it yielded a recognition accuracy of 95.61%. The experimental results showed that the proposed method was feasible and effective for the classification of leaf disease grades.
27 citations
TL;DR: A novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets, and has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques.
Abstract: Imbalanced data refers to a problem in machine learning where there exists unequal distribution of instances for each classes. Performing a classification task on such data can often turn bias in favour of the majority class. The bias gets multiplied in cases of high dimensional data. To settle this problem, there exists many real-world data mining techniques like over-sampling and under-sampling, which can reduce the Data Imbalance. Synthetic Minority Oversampling Technique (SMOTe) provided one such state-of-the-art and popular solution to tackle class imbalancing, even on high-dimensional data platform. In this work, a novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets. It has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques. A critical analysis and comprehensive overview on the literature has been done to get a deeper insight into the problem statements and nurturing the need to obtain the most optimal solution. The performance of NMOTe on some standard datasets has been established in this work to get a statistical understanding on why it has edged the existing state-of-the-art to become the most robust technique for solving the two-class data imbalance problem.
25 citations
TL;DR: In this paper, a fuzzy logic based aerator control system (FLACS) was proposed for waste water treatment, where sensors providing the particulars of the chemical and the biological oxygen demand as input, the microcontroller (Arduino UNO) and other electrical and electronic equipment's that controlled the working of the aerator.
Abstract: The waste water which are the outcomes of processing liquids cannot be used further without proper treatment so the water has to be treated and handled in order to elude the contamination that deteriorates the quality of the atmosphere. Although the biological methods can be utilized for treating the wastes in the water by decomposing the bacteria, it is biased by various causes such as the impurity level, the oxygen available, the dirt type etc. But the standard methodology like aeration utilizes the biological and the chemical oxygen demand reduction termed BO and CO respectively to treat the waste water, the conventional aeration process is performed manually causing enormous usage of electrical energy. So the paper elaborates the scheme of a fuzzy logic based aerator control system (FLACS) for the waste water. The essential equipment of the proposed system are the sensors providing the particulars of the chemical and the biological oxygen demand as input, the microcontroller (Arduino UNO) and other electrical and electronic equipment’s that controls the working of the aerator. The analysis performed on the proffered model indicates the performance improvement of the FLACS in terms of the electrical energy utilization and duration of working hours.
20 citations
TL;DR: In order to have a real time weather forecasting the proposed method tries to develop an automatic weather forecasting device based on the microcontroller that utilizes the sensors to monitor the weather changes and engages the raspberry pi to process the information gathered and convey it to the end user.
Abstract: The entailment for weather forecasting to take the essential pre-cautious measures in our regular routines and elude the unwanted fatalities has made this more attractive area of research. Particularly in the rural areas the weather forecasting enables the farmers to have an effective crop management, avoiding the destruction in the crops and increasing the yield. In order to have a real time weather forecasting the proposed method in the paper tries to develop an automatic weather forecasting device based on the microcontroller. The proposed method utilizes the sensors to monitor the weather changes and engages the raspberry pi to process the information gathered and convey it to the end user. The proposed system was tested by implementing it in the Indian delta districts and the accuracy, precision and flexibility in the forecasting was evinced by the data output observed over and done with the Thinkspeak .Web
20 citations
TL;DR: An electricity power consumption tracking application solution, harnessing both the IoT and Blockchain utilities to provide a decentralized and secure recording mechanism, that provides an improved architecture to the smart meter is proposed in this article.
Abstract: The electricity industry has always been under scrutiny in order to improve the quality of electricity supply, measurement and billing services to have the at most user transparency, while providing these services with the highest efficiency. Although many solutions have emerged, of which the smart meter was considered a viable option, it was quick to perish under the prodigious complications with the real-life feasibilities. El DApp – An electricity power consumption tracking application solution, harnessing both the IoT and Blockchain utilities to provide a decentralized and secure recording mechanism, that provides an improved architecture to the smart meter is proposed in this article. The El DApp provides a high security and cost efficient decentralized live electricity power consumption recording of the user that is maintained by a Raspberry Pi based Ethereum network.
19 citations
TL;DR: An approach to detect deepfake videos of politicians using temporal sequential frames is proposed that uses the forged video to extract the frames at the first level followed by a deep depth-based convolutional long short-term memory model to identify the fake frames atThe second level.
Abstract: Deepfake (a bag of “deep learning” and “fake”) is a technique for human image synthesis based on artificial intelligence, i.e., to superimpose the existing (source) images or videos onto destination images or videos using neural networks (NNs). Deepfake enthusiasts have been using NNs to produce convincing face swaps. Deepfakes are a type of video or image forgery developed to spread misinformation, invade privacy, and mask the truth using advanced technologies such as trained algorithms, deep learning applications, and artificial intelligence. They have become a nuisance to social media users by publishing fake videos created by fusing a celebrity’s face over an explicit video. The impact of deepfakes is alarming, with politicians, senior corporate officers, and world leaders being targeted by nefarious actors. An approach to detect deepfake videos of politicians using temporal sequential frames is proposed. The proposed approach uses the forged video to extract the frames at the first level followed by a deep depth-based convolutional long short-term memory model to identify the fake frames at the second level. Also the proposed model is evaluated on our newly collected ground truth dataset of forged videos using source and destination video frames of famous politicians. Experimental results demonstrate the effectiveness of our method.
18 citations
TL;DR: This paper has contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance, and using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission.
Abstract: One of the most crucial application of Wireless Body Area Networks in healthcare applications is the process of monitoring human bodies and gather physiological data. Network performance degradation in the form of energy efficiency and latency are caused because of energy depletions which arises due to limited energy resource availability. The heterogeneity of body sensors will lead to variation in the rate of energy consumption. Based on this, a novel Data Forwarding Strategy is presented in this research work to enhance collaborative WBAN operations, improve network lifetime and restrict energy consumption of the sensors. In this paper, we have contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance. Using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission. moreover, it is also possible to adapt to the changing WBAN topologies when the proposed methodology is used, balancing energy efficiency and consumption.
18 citations
TL;DR: The motive of this work is to minimize the space requirement for a motor control system by making a wireless communication between the motor driver unit and control unit and the result indicates the reliability and efficiency of the proposed system with various parametric evaluations.
Abstract: Space availability is one of the major parameter to be considered for structuring a control system. The control system installation occupies space for motors, control circuits, wiring connection and driver units. The motive of this work is to minimize the space requirement for a motor control system by making a wireless communication between the motor driver unit and control unit. The design saves the space requirement for control circuit wiring and control circuit unit. The design is helpful in minimizing the overall space requirement of the motor control system. As the control unit is made with wireless communication, the control unit can be moved anywhere near to the system. This improves the accessibility of the system at fault rectification time and precise operation time. The result indicates the reliability and efficiency of the proposed system with various parametric evaluations.
17 citations
TL;DR: Simulation results and security analysis show that the proposed algorithm can resist various cryptanalytic attacks and can be applied to real-time data transmission.
Abstract: We develop a two-dimensional enhanced hyperchaotic Henon map (2D-EHHM) to overcome the problems of small chaotic range and poor security when the 2-D traditional Henon map is implemented in cryptosystems. The performance evaluations show that the 2D-EHHM has wider chaotic range, higher chaotic complexity, better ergodicity, and hyperchaotic behavior compared with certain existing chaotic maps. Based on the 2D-EHHM, we further design an efficient image encryption algorithm consisting of a multiple block substitution stage (MBSS) and a bidirectional-dynamic diffusion stage (BDDS). In the MBSS, a plain image is divided into several nonoverlapping multiple blocks to carry out permutation operation. In the BDDS, the scrambled image is redivided into nonoverlapping sub-blocks of the same size to be diffused dynamically in the forward and backward directions. Moreover, the SHA 512 function is employed to obtain a 512-bit plain image hash value treated as a raw key. A variable-length secret key (at least 128 bits), which is dynamically selected from the raw key and regarded as a valid key, is utilized to generate the initial values for the chaotic system. Simulation results and security analysis show that the proposed algorithm can resist various cryptanalytic attacks and can be applied to real-time data transmission.
TL;DR: A system with an array of integrated sensor modules that continuously measure and record humidity and ambient temperature while simultaneously monitoring the dairy cows drinking behavior using a cost-efficient embedded imaging system to be implemented in dairy farms.
Abstract: It is essential to develop ambient environmental conditions for counteracting the heat stress in dairy cows by efficient and reliable monitoring of the activities of the cow and existing environmental conditions. For this purpose, we present a system with an array of integrated sensor modules that continuously measure and record humidity and ambient temperature while simultaneously monitoring the dairy cows drinking behavior using a cost-efficient embedded imaging system. Video streams are collected by installing embedded imaging modules over the drinking troughs for testing and experimentation in the dairy farm. Convolutional neural network (CNN) model using deep learning techniques is used for analysis of the video stream by detection of the head of the dairy cow above the drinking trough. The values obtained as true positive rate and F1 score of the detection of the head of the cow are both 0.98. The dairy cows drinking behavior and the effect of heat stress is analyzed and recorded for varied environmental conditions over a period of twelve months. Based on the results of analysis, it is evident that the temperature and humidity index (THI) greatly influence the total frequency and length of everyday drinking habits of dairy cows. The drinking behavior of dairy cows and the effects of heat stress is demonstrated clearly using the automated imaging system with long-term monitoring and data collection. Quantitative assessment and automation are possible using this novel monitoring system to be implemented in dairy farms.
TL;DR: The proposed microcontroller based energy saving system is developed to minimize the utilization of light energy in parking spaces in an efficient manner and saves the energy up to 46.35% than the existing lighting system.
Abstract: Vehicles are becoming an essential product in everyone’s life. Keeping a vehicle in a safe place will improve the life of its engine and other electrical systems. Hence, parking place occupies a major portion while constructing a house, apartment and shopping malls. The lighting system in such places are utilizing more energy and it leads to unnecessary expense on electricity bills. The proposed microcontroller based energy saving system is developed to minimize the utilization of light energy in parking spaces in an efficient manner. The results of the proposed system is compared with the general operation for identifying its efficiency. The proposed method saves the energy up to 46.35% than the existing lighting system.
TL;DR: The paper utilizes the costeffective network that is centered on long range technology to automatically assess the degree of fire risky and forest fire rural areas and transmit to the website for public vision using the things network server.
Abstract: The occurrences of forest fires is not only a progressing concern in the lives of the people but also in the deterioration of the environment. Since the emergence of the internet of things, new methodologies are being continuously devised to have an early knowledge about the occurrence of the forest fires. The identifying the areas with the fire risks and the intimating it to the public would minimize the death rate caused due to these types of fire accidents. So the paper utilizes the costeffective network that is centered on long range technology to automatically assess the degree of fire risky and forest fire rural areas and transmit to the website for public vision using the things network server. The proposed method includes many long range nodes and the sensing element to measure the atmospheric changes and the CO2 level in the environment. The long range based sensor network used in the detection of the fire risky and the forest fire areas is evaluated using the network simulator-2 and was found to provide an enhanced service quality by providing a better coverage, battery life, latency, cost and as well as efficiency.
TL;DR: An inner-modal self-attention module is proposed to address the within-part consistency broken problem using both spatial-wise and channel-wise information and a GN is used instead of batch normalization for the accurate batch statistics estimation.
Abstract: Given a natural language description, description-based person re-identification aims to retrieve images of the matched person from a large-scale visual database. Due to the existing modality heterogeneity, it is challenging to measure the cross-modal similarity between images and text descriptions. Many of the existing approaches usually utilize a deep-learning model to encode local and global fine-grained features with a strict uniform partition strategy. This breaks the part coherence, making it difficult to capture meaningful information from the within-part and semantic information among body parts. To address this issue, we proposed an inner-cross-modal attentional multigranular network (IMG-Net) to incorporate inner-modal self-attention and cross-modal hard-region attention with the fine-grained model for extracting the multigranular semantic information. Specifically, the inner-modal self-attention module is proposed to address the within-part consistency broken problem using both spatial-wise and channel-wise information. Following it is a multigranular feature extraction module, which is used to extract rich local and global visual and textual features with the help of group normalization (GN). Then a cross-modal hard-region attention module is proposed to obtain the local visual representation and phrase representation. Furthermore, a GN is used instead of batch normalization for the accurate batch statistics estimation. Comprehensive experiments with ablation analysis demonstrate that IMG-Net achieves the state-of-the-art performance on the CUHK-PEDES dataset and outperforms other previous methods significantly.
TL;DR: In this article, the authors explored the types of challenges encountered by micro, small and medium enterprises to sustain their businesses during the COVID-19 case in early-March 2020, and found that business owners experience difficulties in producing goods and services because of the raw materials shortage, financial liquidity and decreasing demand.
Abstract: Indonesia announced the first COVID-19 case in early of March 2020. Since then, the mobility restriction has hampered the economy, including the micro, small and medium enterprises (MSMEs) businesses. This study aims to explore the types of challenges encountered by MSMEs to sustain their businesses during the hardship. Descriptive qualitative method is used, with data sourced from interview with 34 MSMEs, one in each province in Indonesia, as well as secondary sources including research journals and various reports on the COVID-19 pandemic. The results show that business owners experience difficulties in producing goods and services because of the raw materials shortage, financial liquidity and decreasing demand. Policy and managerial implications of the study are provided.
TL;DR: The paper evaluates certain artificial intelligence based deep learning techniques for finding a suitable approach for monitoring the listener’s activity in real time in the COVID-19 situation.
Abstract: Activity monitoring in online group meetings has become a needed application in the COVID-19 situation. During the lockdown period, most of the teaching classes were conducted through online web applications. The number of attendees in such classes are very higher and it is not to be manageable by a single tutor of the class. The applications are also designed to show only several number of person’s faces in a particular window. To improve the quality of such online classes, it is mandatory to verify the listener’s activity. The paper evaluates certain artificial intelligence based deep learning techniques for finding a suitable approach for monitoring the listener’s activity in real time.
TL;DR: Surveying the current methodologies and the pitfalls in them the paper has developed an efficient and cost effective remote monitoring for unmanned railway gates, enabling automated traffic control technologies and the wireless sensors.
Abstract: Continuous researches are done to improve technologies for developing effective, economical and productive solutions for preventing fatalities in the railway gates that are unmanned. Several solutions in this research field have been suggested but none of them are sufficiently effective to deploy them at the automated level crossings and fully eradicate human presence. Surveying the current methodologies and the pitfalls in them the paper has developed an efficient and cost effective remote monitoring for unmanned railway gates, enabling automated traffic control technologies and the wireless sensors. The paper scope in developing an automated traffic control in the unmanned railway gates in India.
TL;DR: The proposed work deals with a routing algorithm based on trust awareness and compression sensing data, to handle data routing in a clustered WSN.
Abstract: Wireless sensor networks have quickly paved way to novel ways of communication between two nodes. They consist of sensor nodes that have the capacity to sense, communicate and compute. If a particular node in a WSN is not able to transmit data to the base station, routing algorithms will move into action to direct the data from the node. The proposed work deals with a routing algorithm based on trust awareness and compression sensing data, to handle data routing in a clustered WSN. In general, when sensor nodes have reduced overhead, compressed sensing is utilized for data aggregation. In order to strike a balance between number of messages transmitted, hop count, distance of transmission and the optimal trusted path, many nature inspired optimisation methods have been developed over the years. However, trust-based retrieval of compressed data is executed at the base station amidst malicious nodes.
TL;DR: Firman Mansir as mentioned in this paper described the leadership style in a personnel management of an Islamic higher education institution in Yogyakarta, Indonesia, using interview and observation in collecting the data.
Abstract: Leadership is one of crucial things in an education management. It also happens in Islamic higher education institutions, especially in the case of its personnel management. This study aims to describe the leadership style in a personnel management of an Islamic higher education institution in Yogyakarta, Indonesia. This study belongs to qualitative research by using interview and observation in collecting the data. The obtained data then validated by triangulation and descriptively analyzed to produce relevant interpretation of the data. This research comes into a conclusion that the education success is not only measured from the class management, curriculum, students and so on, but also its personnel management. This study 2 Firman Mansir The Leadership of Personnel Management in Islamic Education: Emerging Insights from an Indonesian University DOI: https://doi.org/10.28918/jei.v5i1.2349 promotes that leadership in personnel management of Islamic higher Education needs good leadership style which based on the principle of siddiq, amanah, tabligh, and fathonah. It can be applied through recruitment, development, promotion and transfer, dismissal, compensation, as well as evaluation of employee performance.
TL;DR: The results show the effectiveness of DEH in deal with open-set palmprint recognition, and compared to baseline models, DEH increased the recognition accuracy by up to 6.67% and reduced the equal error rate by 3.48%.
Abstract: Recently, palmprint recognition has made huge progress and attracted the attention of more and more researchers. However, current research rarely involves open-set palmprint recognition. We proposed deep ensemble hashing (DEH) for open-set palmprint recognition. Based on the online gradient boosting model, we trained multiple learners in DEH, which focus on identifying different samples. In order to increase the diversity between learners, activation loss and adversarial loss were introduced. Through minimizing activation loss, the neurons of different learners restrained each other, and through adversarial loss, the optimal distance between the features extracted by different learners was obtained. Palmprint identification and verification experiments were performed on PolyU multispectral database and our self-built databases. The results show the effectiveness of DEH in deal with open-set palmprint recognition. Compared to baseline models, DEH increased the recognition accuracy by up to 6.67% and reduced the equal error rate by up to 3.48%.
TL;DR: The goal of the evaluation is first to compare which algorithm performs better with regard to polarization error and then to investigate both the influence of the dynamic range and number of polarization angle stimuli of the training data.
Abstract: A polarization filter array (PFA) camera is an imaging device capable of analyzing the polarization state of light in a snapshot manner. These cameras exhibit spatial variations, i.e., nonuniformity, in their response due to optical imperfections introduced during the nanofabrication process. Calibration is done by computational imaging algorithms to correct the data for radiometric and polarimetric errors. We reviewed existing calibration methods and applied them using a practical optical acquisition setup and a commercially available PFA camera. The goal of the evaluation is first to compare which algorithm performs better with regard to polarization error and then to investigate both the influence of the dynamic range and number of polarization angle stimuli of the training data. To our knowledge, this has not been done in previous work.
TL;DR: This paper presents an optimal evaluation technique that involves the environmental criteria and can be implemented in the future energy policy and shows reduced emission of CO2 and 75% energy saving using the best solution.
Abstract: Ineffective policies, missing technical information, large volumes of inappropriate luminaires, malpractice and several such reasons act as a hinderance for the adoption of LEDs in road lighting design despite being the most efficient sources of light. In national roads, the decision makers are sometimes confused by the low efficacy values of the luminaires. The tools for lighting simulation and projects in street lights require several energy performance indicators as described in EN13201-5 which is a novel system. This paper presents an optimal evaluation technique that involves the environmental criteria and can be implemented in the future energy policy. Evaluation of lighting tender and lighting designs is performed using a decision tool while analysing the significance of these factors. The corresponding offers and their ranking is evaluated by the decision tool. Several environmental benefits as well as improved energy saving can be achieved on implementation of this system. Simulation results shows reduced emission of CO2 and 75% energy saving using the best solution.
TL;DR: The proposed model is designed to minimize the energy consumption with maximum human comfort and several sensor modules were introduced in the model to predict human comfort level in a room or hall.
Abstract: Air conditioning systems were invented to improve human comfort in a room or hall. An efficient air conditioner systems are always needed to minimize the power consumption. There are several settings with lot of control devices were introduced in the past years to achieve minimal energy consumption rate. Those control systems were minimized the energy consumption to certain limit without considering human comfort. The proposed model is designed to minimize the energy consumption with maximum human comfort. Several sensor modules were introduced in the model to predict human comfort level in a room or hall. The sensor data are taken as feedback to the air conditioning system for attaining maximum human comfort level. The proposed design is verified with energy consumption calculation and change in room temperature measurements.
TL;DR: The proposed deep transfer-based learning has achieved phenomenal recognition rates for PashTo ligatures on benchmark FAST-NU Pashto dataset.
Abstract: Over the past decades, text recognition technologies have focused immensely on noncursive isolated scripts. A text recognition system for the cursive Pashto script will serve as a great contribution, allowing the traditional, cultural, and educational Pashto literature to be converted into machine-readable form. We propose the use of deep learning architectures based on the transfer learning for the recognition of Pashto ligatures. For recognition analysis and evaluation, the ligature images in the dataset are preprocessed by data augmentation techniques, i.e., negatives, contours, and rotated to increase the variation of each sample and size of the original dataset. Rich feature representations are automatically extracted from the Pashto ligature images using deep convolution layers of the convolution neural network (CNN) architectures using fine-tuned approach. Pretrained CNN architectures: AlexNet, GoogleNet, and VGG (VGG-16 and VGG-19) are used for classification by feeding the extracted features to a fully connected layer and a softmax layer. The proposed deep transfer-based learning has achieved phenomenal recognition rates for Pashto ligatures on benchmark FAST-NU Pashto dataset. An accuracy of 97.24%, 97.46%, and 99.03% is achieved using AlexNext, GoogleNet, and VGGNet architectures, respectively.
TL;DR: In this paper, the relationship between the levels of 5th grade science course exam questions and the 5th class learning outcomes of the science curriculum in the revised Bloom taxonomy was examined.
Abstract: In this study, the relationship between the levels of 5 th grade science course exam questions and the 5 th class learning outcomes of the science curriculum in the revised Bloom taxonomy was examined. The research was carried out using document analysis method. Since the revised Bloom taxonomy categories were used for the analysis, the data obtained were analyzed with the descriptive analysis technique. The study included 967 science questions and 40 learning outcomes in the 2017-2018 academic year. These questions and learning outcomes were analyzed. At the end of analysis, the relationship between the learning outcomes and exam questions was determined. The inter-rater reliability computing has been made in the analysis of questions-learning outcomes. The reliability co-efficient was calculated .81 for learning outcomes and .77 for questions, indicating an acceptable reliability. According to the results of the analysis, it was determined that the most learning outcomes were in the conceptual knowledge dimension and the most questions were included in the factual knowledge dimension. In the cognitive processes dimension, it was determined that most learning outcomes are at the level of understanding, and the most questions are at the level of remembering. It is understood that 37% of the exam questions are at the level of learning outcomes. In addition, it was determined that there were no questions about some learning outcomes (24%).
TL;DR: In this paper, the authors investigated the beliefs of Turkish EFL teachers about implementing digital technology in the classroom and the underlying factors which affect their beliefs, and found that teachers shared positive views on the use of digital technology on EFL classrooms in terms of importance, use, expertise, and context.
Abstract: This study aims to investigate the beliefs of Turkish EFL teachers about implementing digital technology in the classroom and the underlying factors which affect their beliefs. Quantitative research design was selected and online Beliefs Questionnaire was used for data collection. The participants of the study consisted of 563 Turkish in-service EFL teachers working at state schools, private schools or colleges in various parts of Turkey. Descriptive statistics and correlation analysis were performed to determine whether the participants’ four categorized beliefs about the use of digital technology ( i.e. importance, use, expertise, and context) interrelated with each other, and if any of the participants’ demographic and background factors ( i.e. age, gender, level of education, years of teaching experience) predicted the reported beliefs. The results indicated that teachers shared positive views on the use of digital technology in EFL classrooms in terms of importance, use, expertise, and context. It was also found that gender, age and teaching experience did not create any significant change on teachers’ beliefs.
TL;DR: The main goal of this paper is to demonstrate batch process control structured ladder logic with emphasis on key safety and quality design issues.
Abstract: Batch process control is typically used for repeated chemical reaction tasks. It starts with a measured liquid material filling operations followed by a controlled reaction leading to the discharge or transport of processed quantities of material. The input materials is contained in vessel reactor and subjected to a sequence of processing activities over a recipe predefined duration of time. Batch systems are designed to measure, process, and discharge a varying volume of liquid from drums, tanks, reactors, or other large storage vessel using a programmable logic controller (PLC). These systems are common in pharmaceutical, chemical packaging, Beverage processing, personal care product, biotech manufacturing, dairy processing, soap manufacturing, and food processing industries. This paper briefly discusses the fundamental techniques used in specifying, designing, and implementing a PLC batch process control [1, 2]. A simplified batch process is used to illustrate key issues in designing and implementing such systems. In addition to the structured PLC ladder design; more focus is given to safety requirements, redundancy, interlocking, input data validation, and safe operation. The Allen Bradley (AB) SLC 500 PLC along with the LogixPro simulator are used to illustrate the concepts discussed in this paper. Two pumps are used to bring in material during the tank filling and a third pump is used to drain processed product. The three pumps are equipped with flow meters providing pulses proportional to the actual flow rate through the individual pipes. The tank material is heated to a predefined temperature duration followed by mixing for a set time before discharge. Batch control systems provides automated process controls, typically and universally using PLC’s networked to HMI’s and other data storage, analysis, and assessment computers. The overall system perform several tasks including recipe development and download, production scheduling, batch management and execution, equipment performance monitoring, inventory, production history and tracking functionalities. Flexible batch control systems are designed to accommodate smaller batches of products with greater requirements / recipes variation, efficiently and quickly. In addition to providing process consistency, continuous batch process control quality improvements are attained through the automatic collection and analysis of real-time reliable and accurate event performance data [3, 4]. I. Batch Process Description In this illustration a small and abbreviated part of a real batch automation project involving the heating and mixing of two liquid chemicals in certain amounts and under pre-specified conditions. Figure 1 shows the main page of an HMI representation of the implemented system using Allen Bradley SLC 500 LogixPro simulator [5, 6]. The main goal of this paper is to demonstrate batch process control structured ladder logic with emphasis on key safety and quality design issues. Journal of Electronics and Informatics (2020) Vol.02/ No.03 Pages: 155-161 http://www.irojournals.com/iroei/ DOI: https://doi.org/10.36548/jei.2020.3.001 156 ISSN: 2582-3825 (online) Submitted: 14.04.2020 Accepted: 01.06.2020 Published: 09.06.2020 Figure 1 Batch System Main HMI Page The following tables list all inputs, outputs, and internal reference addresses used in this abbreviated batch control task; physical address, tag name, and the associated functions.
TL;DR: The proposed artificial intelligence based management and control system consists of several sensor elements and wireless IoT transmission to predict and avoid the fault occurrence by monitoring the physical and atmosphere condition of the transmission and distribution line.
Abstract: The electrical transmission and distribution systems are working on their own independence in operation. The operation of these systems can be modified by manual switching process. The switching process takes place only there is a need for transmission line alteration and transmission line fault attendance period. The manual switching operation during fault occurrence period consumes lot of time for the trained person to reach the place and it may leads to severe damages to the transmission system, also it’s a threat to human safety. In order to avoid such drawbacks circuit breakers and automatic trippers were installed to the transmission lines and distribution systems. The circuit breakers and trippers are able to switch off the system only after the fault observation in the transmission line system. The proposed artificial intelligence based management and control system consists of several sensor elements and wireless IoT transmission to predict and avoid the fault occurrence by monitoring the physical and atmosphere condition of the transmission and distribution line. The control structure fitted with the transmission line monitors the environment and line fault condition and the IoT transmission unit gives a possible communication from the remote monitoring system to the transmission line system for switching operations.