Search results for: one side class algorithm

Authors: Mohamed Moheyeldin Mahmoud

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Egypt suffers from a severe shortage of data and charts concerning the physical dimensions, measurements, qualities and consumer behavior. The shortage of needed information and appropriate methods has forced the Egyptian designer to use any other foreign standard when designing a product for the Egyptian consumer which has led to many problems. The urgently needed database concerning the physical specifications, measurements of the Egyptian consumers, as well as the need to support the Ergonomics given courses in many colleges and institutes with the latest technologies, is stated as the research problem. Descriptive analytical method relying on the compiling, comparing and analyzing of information and facts in order to get acceptable perceptions, ideas and considerations is the used methodology by the researcher. The research concludes that: 1. Good interaction relationship between users and products shows the success of that product. 2. An integration linkage between the most prominent fields of science specially Ergonomics, Interaction Design and Ethnography should be encouraged to provide an ultimately updated database concerning the nature, specifications and environment of the Egyptian consumer, in order to achieve a higher benefit for both user and product. 3. Chinese economic policy based on the study of market requirements long before any market activities should be emulated. 4. Using Ethnography supports the design activities creating new products or updating existent ones through measuring the compatibility of products with their environment and user expectations, While contracting a joint cooperation between military colleges, sports education institutes from one side, and design institutes from the other side to provide an ultimately updated (annually updated) database concerning some specifications about students of both sexes applying in those institutes (height, weight, etc.) to provide the Industrial designer with the needed information when creating a new product or updating an existing one concerning that category is recommended by the researcher.

Keywords: adapt, ergonomics, ethnography, interaction design

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Authors: Efe Çukur

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This study aims to discuss and compare i) widespread and generic design, particularly industrial design education in relation to the specific needs of the automotive/transportation industry, and ii) an automotive/transportation design education model within and under to provide the conditions of design education and automotive industry, especially in Turkey and T.R.N.C. The automotive industry is the 11th largest in the world ($1.51 trillion). One of the most important departments in this industry, along with sales, marketing and engineering, is the design department. The automotive industry is known as the locomotive industry, but there is a non-automotive design department on the academic side of Turkey. This suggestion; includes the presentation of a program proposal that meets the needs of the industry for Turkey and T.R.N.C., the second largest automobile manufacturing country in Europe. On the education side, industrial design education has become a generic title. Automotive design studios are divided into several subgroups. Even in the higher graduate education, the automotive design departments get their subgroups like exterior design and interior design. Transportation design, which is a subfield of industrial design, is offered as higher education in transportation design departments, particularly in America and Europe. In these departments, the curriculum is shaped to the needs of the sectors. Higher education transportation design programs began in the mid-20th century. Until those high education programs...Until these high education programs, the industry has adapted architectures and engineers for designer workloads. Still today transportation design graduates are not the majority of the design studios. The content of the study is an in-depth comparison of these institutions and how the requirements, demands of the industry are met in this regard and revealed. Some of the institutions are selected from Europe and US. To be analyzed under the headings of staff, courses, syllabus, University-Industry collaboration, and location selection. The study includes short, mid, and long term proposals and a hypothesis for discussion. In short, the study will not only provide a wide comparative scope of information on generic and specialized aspects of design education in different countries but also propose a higher education model for automotive / transportation design with solid data of requirements, methodology, and structure regarding learning outcomes, and especially industry cooperation.

Keywords: design education, automotive - transportation design programs, transportation design, automotive industry in Turkey /T.R.N.C., automotive design education in Turkey /T.R.N.C.

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Authors: Elizabeth Malebogo Mosepele

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Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

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Authors: Mustafa El-Halabi

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A general model of wiretap channel with states is considered, where the legitimate receiver and the wiretapper’s observations depend on three states S1, S2 and S3. State S1 is non-causally known to the encoder, S2 is known to the receiver, and S3 remains unknown. A secure coding scheme, based using structured-binning, is proposed, and it is shown to achieve the secrecy capacity when the signal at legitimate receiver is a deterministic function of the input.

Keywords: physical layer security, interference, side information, secrecy capacity

Procedia PDF Downloads 392

Authors: Satoko Yamasaki, Hiromi Kawasaki, Kotomi Yamashita, Susumu Fukita, Kei Sounai

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People are susceptible to rumors when the cause of their health problems is unknown or invisible. In order for individuals to be unaffected by rumors, they need basic knowledge and correct information. Community health nursing classes use cases where basic knowledge of radiation can be utilized on a regular basis, thereby teaching that basic knowledge is important in preventing anxiety caused by rumors. Nursing students need to learn that preventive activities are essential for public health nursing care. This is the same methodology used to reduce COVID-19 anxiety among individuals. This study verifies the learning effect concerning the basic knowledge of radiation necessary for case consultation by emergency distance learning. Sixty third-year nursing college students agreed to participate in this research. The knowledge tests conducted before and after classes were compared, with the chi-square test used for testing. There were five knowledge questions regarding distance lessons. This was considered to be 5% significant. The students’ reports which describe the results of responding to health consultations, were analyzed qualitatively and descriptively. In this case study, a person living in an area not affected by radiation was anxious about drinking water and, thus, consulted with a student. The contents of the lecture were selected the minimum amount of knowledge used for the answers of the consultant; specifically hot spots, internal exposure risk, food safety, characteristics of cesium-137, and precautions for counselors. Before taking the class, the most correctly answered question by students concerned daily behavior at risk of internal exposure (52.2%). The question with the fewest correct answers was the selection of places that are likely to be hot spots (3.4%). All responses increased significantly after taking the class (p < 0.001). The answers to the counselors, as written by the students, were 'Cesium is strongly bound to the soil, so it is difficult to transfer to water' and 'Water quality test results of tap water are posted on the city's website.' These were concrete answers obtained by using specialized knowledge. Even in emergency distance learning, the students gained basic knowledge regarding radiation and created a document to utilize said knowledge while assuming the situation concretely. It was thought that the flipped classroom method, even if conducted remotely, could maintain students' learning. It was thought that setting specific knowledge and scenes to be used would enhance the learning effect. By changing the case to concern that of the anxiety caused by infectious diseases, students may be able to effectively gain the basic knowledge to decrease the anxiety of residents due to infectious diseases.

Keywords: effect of class, emergency distance learning, nursing student, radiation

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Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks

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Authors: Ikram Mohamed Eltayeb, Ustina Saeed Barsoumbolice

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Cinnamomum zeylanicum belong to the family Lauraceae and Artemisia absinthium belong to the family Asteraceae. Both were traditionally used as antiemetic, anti-inflammatory and antidiabetic. In Sudan, the mixtures of the two plants were traditionally used for the treatment of diabetes. Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia. It is mainly classified into two major groups, type-1 and type-2. Type-2 is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. The treatment of type-2 diabetes mellitus (DM) with synthetic drugs have many side effects so many researches were conducted to overcome or reduce this side effects by using alternative medicine. The objective of this study is to investigate and compare the anti-diabetic activity of C. zeylanicum and A. absinthium and their combination with difference ratio. C. zeylanicum and A. absinthium were extracted by 96% ethanol using Soxhlet apparatus. Thirty-two rats were divided into eight groups; each group contains four rats. 1st group was administered with distilled water at dose of 10ml/kg, 2nd group had received glucose only at dose of 2g/kg intraperitoneal, the standard group (3rd group) had received Glibenclamide orally at dose of 0.45mg/kg, 4th group received 100 mg C. zeylanicum + 300 mg A. absinthium with a ratio of (25:75), 5th group received 300 mg C. zeylanicum + 100 mg A. absinthium with a ratio of (75:25), 6th group received 200 mg C. zeylanicum + 200 mg A. absinthiumwith a ratio of (50:50), 7th group received 400 mg of A. absinthium, 8th group received 400 mg of C. zeylanicum. Then the blood samples were taken Retro-orbitally at 0, 1, 2 and 4 hours and the glucose level was measured. Each plant alone and their combination with different ratios shows antidiabetic effect. The significant activity was shown by A. absinthium extract (400 mg/kg), combination of ratio of (75:25) A. absinthium: C. zeylanicum(400mg/kg) and then C. zeylanicum(400mg/kg) with p-value 0.001, 0.022, 0.030 respectively, the activity was found to be increased with time. The other combinations showed less activity with p-value > 0.05. The result concludes that the good antidiabetic activity was performed by A. absinthium alone and its activity decreased by increase combination ratio with C. zeylanicum. Which maybe explains by the antagonistic effect between the compounds of C. zeylanicum and A. absinthium.

Keywords: antidiabetic, Artemisia absinthium , cinnamomum zeylanicum, combination

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Authors: Reiko Yamamoto

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This paper reports on how synchrony occurs between children and their teacher, and what prevents or facilitates synchrony. The aim of the experiment conducted in this study was to precisely analyze their movements and synchrony and reveal the process of synchrony in a real-world classroom. Specifically, the experiment was conducted for around 20 minutes during an English as a foreign language (EFL) lesson. The participants were 11 fourth-grade school children and their classroom teacher in a public elementary school in Japan. Previous researchers assert that synchrony causes the state of flow in a class. For checking the level of flow, Short Flow State Scale (SFSS) was adopted. The experimental procedure had four steps: 1) The teacher read aloud the first half of an English storybook to the children. Both the teacher and the children were at their own desks. 2) The children were subjected to an SFSS check. 3) The teacher read aloud the remaining half of the storybook to the children. She made the children remove their desks before reading. 4) The children were again subjected to an SFSS check. The movements of all participants were recorded with a video camera. From the movement analysis, it was found that the children synchronized better with the teacher in Step 3 than in Step 1, and that the teacher’s movement became free and outstanding without a desk. This implies that the desk acted as a barrier between the children and the teacher. Removal of this barrier resulted in the children’s reactions becoming synchronized with those of the teacher. The SFSS results proved that the children experienced more flow without a barrier than with a barrier. Apparently, synchrony is what caused flow or social emotions in the classroom. The main conclusion is that synchrony leads to cognitive outcomes such as children’s academic performance in EFL learning.

Keywords: engagement in a class, English as a foreign language (EFL) learning, interactional synchrony, social emotions

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

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Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

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Authors: Paul Hernandez-Herrera, Fernando Montoya, Juan Manuel Rendon, Alberto Darszon, Gabriel Corkidi

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Intracellular calcium ([Ca2+]i) regulates sperm motility. The analysis of [Ca2+]i has been traditionally achieved in two dimensions while the real movement of the cell takes place in three spatial dimensions. Due to optical limitations (high speed cell movement and low light emission) important data concerning the three dimensional movement of these flagellated cells had been neglected. Visualizing [Ca2+]i in 3D is not a simple matter since it requires complex fluorescence microscopy techniques where the resulting images have very low intensity and consequently low SNR (Signal to Noise Ratio). In 4D sequences, this problem is magnified since the flagellum oscillates (for human sperm) at least at an average frequency of 15 Hz. In this paper, a novel approach to extract the flagellum’s center-line in 4D stacks is presented. For this purpose, an iterative algorithm based on the fast-marching method is proposed to extract the flagellum’s center-line. Quantitative and qualitative results are presented in a 4D stack to demonstrate the ability of the proposed algorithm to trace the flagellum’s center-line. The method reached a precision and recall of 0.96 as compared with a semi-manual method.

Keywords: flagellum, minimal path, segmentation, sperm

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: P. Joshna, Souvik Kundu

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Detection of ultraviolet (UV) radiation is very important as it has exhibited a profound influence on humankind and other existences, including military equipment. In this work, a self-powered UV photodetector was reported based on oxides heterojunctions. The thin films of p-type nickel oxide (NiO) and n-type reduced graphene oxide (rGO) were used for the formation of p-n heterojunction. Low-Cost and low-temperature chemical synthesis was utilized to prepare the oxides, and the spin coating technique was employed to deposit those onto indium doped tin oxide (ITO) coated glass substrates. The top electrode platinum was deposited utilizing physical vapor evaporation technique. NiO offers strong UV absorption with high hole mobility, and rGO prevents the recombination rate by separating electrons out from the photogenerated carriers. Several structural characterizations such as x-ray diffraction, atomic force microscope, scanning electron microscope were used to study the materials crystallinity, microstructures, and surface roughness. On one side, the oxides were found to be polycrystalline in nature, and no secondary phases were present. On the other side, surface roughness was found to be low with no pit holes, which depicts the formation of high-quality oxides thin films. Whereas, x-ray photoelectron spectroscopy was employed to study the chemical compositions and oxidation structures. The electrical characterizations such as current-voltage and current response were also performed on the device to determine the responsivity, detectivity, and external quantum efficiency under dark and UV illumination. This p-n heterojunction device offered faster photoresponse and high on-off ratio under 365 nm UV light illumination of zero bias. The device based on the proposed architecture shows the efficacy of the oxides heterojunction for efficient UV photodetection under zero bias, which opens up a new path towards the development of self-powered photodetector for environment and health monitoring sector.

Keywords: chemical synthesis, oxides, photodetectors, spin coating

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Authors: Muhammad R. Ahmed, Mohammed Aseeri

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Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level.

Keywords: internal attack, wireless sensor network, network security, entropy

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Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

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The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

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Authors: K. K. Soundra Pandian, Bhupendra Gupta

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In the modern era, as the application data’s are massive and complex, it needs to be secured from the adversary attack. In this context, a non-recursive key based integrated spritz stream cipher with the circulant hash function using delayed feedback with carry shift register (d-FCSR) is proposed in this paper. The novelty of this proposed stream cipher algorithm is to engender the improved keystream using d-FCSR. The proposed algorithm is coded using Verilog HDL to produce dynamic binary key stream and implemented on commercially available FPGA device Virtex 5 xc5vlx110t-2ff1136. The implementation of stream cipher using d-FCSR on the FPGA device operates at a maximum frequency of 60.62 MHz. It achieved the data throughput of 492 Mbps and improved in terms of efficiency (throughput/area) compared to existing techniques. This paper also briefs the cryptanalysis of proposed circulant hash value based spritz stream cipher using d-FCSR is against the adversary attack on a hardware platform for the hardware based cryptography applications.

Keywords: cryptography, circulant function, field programmable gated array, hash value, spritz stream cipher

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Authors: Nam Kyu Kim, Hee Jun Cha, Jae Jin Seo, Dong Jun Won

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Output power of a photovoltaic (PV) generator depends on incident solar irradiance. If the clouds pass or the climate condition is bad, the PV output fluctuates frequently. When PV generator is connected to the grid, these fluctuations adversely affect power quality. Thus, ramp rate control with battery energy storage system (BESS) is needed to reduce PV output fluctuations. At the same time, for effective BESS operation and sizing the optimal BESS capacity, managing state of charge (SOC) is the most important part. In addition, managing SOC helps to avoid violating the SOC operating range of BESS when performing renewable integration (RI) continuously. As PV and BESS increase, the SOC management of BESS will become more important in the future. This paper presents the SOC management algorithm which helps to operate effectively BESS, and has focused on method to manage SOC while reducing PV output fluctuations. A simulation model is developed in PSCAD/EMTDC software. The simulation results show that the SOC is maintained within the operating range by adjusting the output distribution according to the SOC of the BESS.

Keywords: battery energy storage system, ramp rate control, renewable integration, SOC management

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Authors: P. Neeraj, Anurag Sharma, Harsukhpreet Singh

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In current years, Brain-Computer Interface (BCI) scheme based on steady-state Visual Evoked Potential (SSVEP) have earned much consideration. This study tries to evolve an SSVEP based BCI scheme that can regulate any gadget mock-up in two unique positions ON and OFF. In this paper, two distinctive gleam frequencies in low-frequency part were utilized to evoke the SSVEPs and were shown on a Liquid Crystal Display (LCD) screen utilizing Lab View. Two stimuli shading, Yellow, and Blue were utilized to prepare the system in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital part. Elements of the brain were separated by utilizing discrete wavelet Transform. A prominent system for multilayer system diverse Neural Network Algorithm (NNA), is utilized to characterize SSVEP signals. During training of the network with diverse calculation Regression plot results demonstrated that when Levenberg-Marquardt preparing calculation was utilized the exactness turns out to be 93.9%, which is superior to another training algorithm.

Keywords: brain computer interface, electroencephalography, steady-state visual evoked potential, wavelet transform, neural network

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Authors: Minsu Kim, Hae-Chang Cho, Jae Hoon Chung, Inwook Heo, Kang Su Kim

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For safe design of the UHPC flexural members, accurate estimations of their shear strengths are very important. However, since the shear strengths are significantly affected by various factors such as tensile strength of concrete, shear span to depth ratio, volume ratio of steel fiber, and steel fiber factor, the accurate estimations of their shear strengths are very challenging. In this study, therefore, the Adaptive Neuro-Fuzzy System (ANFIS), which has been widely used to solve many complex problems in engineering fields, was introduced to estimate the shear strengths of UHPC flexural members. A total of 32 experimental results has been collected from previous studies for training of the ANFIS algorithm, and the well-trained ANFIS algorithm provided good estimations on the shear strengths of the UHPC test specimens. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2016R1A2B2010277).

Keywords: ultra-high-performance concrete, ANFIS, shear strength, flexural member

Procedia PDF Downloads 194

Authors: Tao Yin, Zeqiang Zhang, Wei Liang, Yanqing Zeng, Yu Zhang

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To accelerate the remanufacturing process of electronic waste products, this study designs a partial disassembly line with the multi-robotic station to effectively dispose of excessive wastes. The multi-robotic partial disassembly line is a technical upgrade to the existing manual disassembly line. Balancing optimization can make the disassembly line smoother and more efficient. For partial disassembly line balancing with the multi-robotic station (PDLBMRS), a mixed-integer programming model (MIPM) considering the robotic efficiency differences is established to minimize cycle time, energy consumption and hazard index and to calculate their optimal global values. Besides, an enhanced NSGA-II algorithm (HNSGA-II) is proposed to optimize PDLBMRS efficiently. Finally, MIPM and HNSGA-II are applied to an actual mixed disassembly case of two types of computers, the comparison of the results solved by GUROBI and HNSGA-II verifies the correctness of the model and excellent performance of the algorithm, and the obtained Pareto solution set provides multiple options for decision-makers.

Keywords: waste disposal, disassembly line balancing, multi-robot station, robotic efficiency difference, HNSGA-II

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Authors: Pin-Ling Yin, Dung-Ying Lin

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Satellites play an important role in our daily lives, from monitoring the Earth's environment and providing real-time disaster imagery to predicting extreme weather events. As technology advances and demands increase, the tasks undertaken by satellites have become increasingly complex, with more stringent resource management requirements. A common challenge in satellite mission scheduling is the limited availability of resources, including onboard memory, ground station accessibility, and satellite power. In this context, efficiently scheduling and managing the increasingly complex satellite missions under constrained resources has become a critical issue that needs to be addressed. The core of Satellite Onboard Activity Planning (SOAP) lies in optimizing the scheduling of the received tasks, arranging them on a timeline to form an executable onboard mission plan. This study aims to develop an optimization model that considers the various constraints involved in satellite mission scheduling, such as the non-overlapping execution periods for certain types of tasks, the requirement that tasks must fall within the contact range of specified types of ground stations during their execution, onboard memory capacity limits, and the collaborative constraints between different types of tasks. Specifically, this research constructs a mixed-integer programming mathematical model and solves it with a commercial optimization package. Simultaneously, as the problem size increases, the problem becomes more difficult to solve. Therefore, in this study, a heuristic algorithm has been developed to address the challenges of using commercial optimization package as the scale increases. The goal is to effectively plan satellite missions, maximizing the total number of executable tasks while considering task priorities and ensuring that tasks can be completed as early as possible without violating feasibility constraints. To verify the feasibility and effectiveness of the algorithm, test instances of various sizes were generated, and the results were validated through feedback from on-site users and compared against solutions obtained from a commercial optimization package. Numerical results show that the algorithm performs well under various scenarios, consistently meeting user requirements. The satellite mission scheduling algorithm proposed in this study can be flexibly extended to different types of satellite mission demands, achieving optimal resource allocation and enhancing the efficiency and effectiveness of satellite mission execution.

Keywords: mixed-integer programming, meta-heuristics, optimization, resource management, satellite mission scheduling

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: F. J. Ma, A. K. H. Kwan

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Crack caused by shrinkage movement of concrete is a serious problem especially when restraint is provided. It may cause severe serviceability and durability problems. The existing prediction methods for crack width of concrete due to shrinkage movement are mainly numerical methods under simplified circumstances, which do not agree with each other. To get a more unified prediction method applicable to more sophisticated circumstances, finite element crack width analysis for shrinkage effect should be developed. However, no existing finite element analysis can be carried out to predict the crack width of concrete due to shrinkage movement because of unsolved reasons of conventional finite element analysis. In this paper, crack width analysis implemented by finite element analysis is presented with pseudo-discrete crack model, which combines traditional smeared crack model and newly proposed crack queuing algorithm. The proposed pseudo-discrete crack model is capable of simulating separate and single crack without adopting discrete crack element. And the improved finite element analysis can successfully simulate the stress redistribution when concrete is cracked, which is crucial for predicting crack width, crack spacing and crack number.

Keywords: crack queuing algorithm, crack width analysis, finite element analysis, shrinkage effect

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Authors: Fiona Bei Na Tan, Glen Wen Kiat Ho, Ee Leen Liow, Li Yin Tan, Sean Wei Loong Ho

Abstract:

Background: Shoulder arthroscopy is an increasingly common procedure. Pneumothorax post-shoulder arthroscopy is a rare complication. Objectives: Our aim is to highlight a case report of pneumothorax post shoulder arthroscopy and to conduct a literature review to evaluate the possible risk factors associated with developing a pneumothorax during or after shoulder arthroscopy. Case Report: We report the case of a 75-year-old male non-smoker who underwent left shoulder arthroscopy without regional anaesthesia and in the left lateral position. The general anaesthesia and surgery were uncomplicated. The patient was desaturated postoperatively and was found to have a pneumothorax on examination and chest X-ray. A chest tube drain was inserted promptly into the right chest. He had an uncomplicated postoperative course. Methods: PubMed Medline and Cochrane database search was carried out using the terms shoulder arthroplasty, pneumothorax, pneumomediastinum, and subcutaneous emphysema. We selected full-text articles written in English. Results: Thirty-two articles were identified and thoroughly reviewed. Based on our inclusion and exclusion criteria, 14 articles, which included 20 cases of pneumothorax during or after shoulder arthroscopy, were included. Eighty percent (16/20) of pneumothoraxes occurred postoperatively. In the articles that specify the side of pneumothorax, 91% (10/11) occur on the ipsilateral side of the arthroscopy. Eighty-eight percent (7/8) of pneumothoraxes occurred when subacromial decompression was performed. Fifty-six percent (9/16) occurred in patients placed in the lateral decubitus position. Only 30% (6/20) occurred in current or ex-smokers, and only 25% (5/20) had a pre-existing lung condition. Overall, of the articles that posit a mechanism, 75% (9/12) deem the pathogenesis to be multifactorial. Conclusion: The exact mechanism of pneumothorax is currently unknown. Awareness of this complication and timely recognition are important to prevent life-threatening sequelae. Surgeons should have a low threshold to obtain diagnostic plain radiographs in the event of clinical suspicion.

Keywords: rotator cuff repair, decompression, pressure, complication

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Authors: Rujuta Matapurkar, Shivganesh Bhargava

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This paper discusses work-family enrichment and its relationship with certain antecedents and outcomes while considering effect of mindfulness and organizational pride as moderators. The work-family enrichment has been the topic of interest for researchers as well as practitioners for decades now. It focusses on the positive side of work family interaction rather that the scarcity or balance principle. Research shows that work family enrichment is linked to multiple work place outcomes like job satisfaction, organization citizenship behavior and turnover intention. Enrichment is also linked to life outcomes like life satisfaction, wellbeing. Thus not only the individuals but the organizations too want to engage in the activities resulting in the positive spillover between work and non-work domains. One of the recent focus areas in organization behavior literature has been Mindfulness. Mindfulness is defined as a trait or state in which the mind focuses on the present. It is the conscious attention and awareness of the present thought. The research in the area of mindfulness at work suggests that the same is related to work family balance and job satisfaction. This paper discusses the possibility of mindfulness having effect on the relationship between antecedents of enrichment and enrichment. On the outcome side job embeddedness and job ambivalence are the newest additions to the retention literature. Job ambivalence talks about having strong positive as well as negative feelings about the job. Job ambivalence is the work outcome which is linked to turnover intention. This paper talks about the relationship between enrichment and job ambivalence. Another measure for work place outcomes which is discussed in recent research is job embeddedness. This term talks about the advantages of continuing with the job rather than quitting it. It is described as like a net or a web in which an individual can become stuck and is focused on why people stay rather than on how they leave. The research has have found that establishing or increasing job embeddedness is likely to increase retention, attendance, citizenship and job performance. This paper studies the relationship between enrichment and embeddedness. Lastly this paper studies whether organizational pride has an an effect on the relationship between enrichment and its outcomes. This paper concludes with the direction for future research.

Keywords: work-family enrichment, mindfulness, job ambivalence, job embeddedness, organizational pride

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Authors: Elissar Mansour, Emily Chen, Tracee Fernandez, Mariam Basheti, Christopher Gordon, Bandana Saini

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Sleep is an essential process for the maintenance of several neurobiological processes such as memory consolidation, mood, and metabolic processes. It is known that sleep patterns vary with age and is affected by multiple factors. While non-pharmacological strategies are generally considered first-line, sedatives are excessively used in the older population. This study aimed to explore the management of sleep in residential aged care facilities (RACFs) by nurse professionals and to identify the key factors that impact provision of optimal sleep health care. An inductive thematic qualitative research method was employed to analyse the data collected from semi-structured interviews with registered nurses working in RACF. Seventeen interviews were conducted, and the data yielded three themes: 1) the nurses’ observations and knowledge of sleep health, 2) the strategies employed in RACF for the management of sleep disturbances, 3) the organizational barriers to evidence-based sleep health management. Nurse participants reported the use of both non-pharmacological and pharmacological interventions. Sedatives were commonly prescribed due to their fast action and accessibility despite the guidelines indicating their use in later stages. Although benzodiazepines are known for their many side effects, such as drowsiness and oversedation, temazepam was the most commonly administered drug. Sleep in RACF was affected by several factors such as aging and comorbidities (e.g., dementia, pain, anxiety). However, the were also many modifiable factors that negatively impacted sleep management in RACF. These include staffing ratios, nursing duties, medication side effects, and lack of training and involvement of allied health professionals. This study highlighted the importance of involving a multidisciplinary team and the urge to develop guidelines and training programs for healthcare professionals to improve sleep health management in RACF.

Keywords: registered nurses, residential aged care facilities, sedative use, sleep

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Karima Djebaili, Lamine Melkemi

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Elgamal encryption is a fundamental public-key encryption in cryptography, which is based on the difficulty of discrete logarithm problem and the Diffie-Hellman problem. Supposing the Diffie–Hellman problem is computationally infeasible then Elgamal is secure under a chosen plaintext attack, where security indicates it is difficult for the attacker, given the ciphertext, to restore the whole of the plaintext. However, although it is secure against chosen plaintext attack, Elgamal is absolutely malleable i.e. is not secure against an adaptive chosen ciphertext attack, where the attacker can recover the plaintext. We present a extension on Elgamal encryption which result in non-malleability against adaptive chosen plaintext attack using concatenation and a cryptographic hash function, our evidence utilizes the device of plaintext aware. The algorithm proposed can be used in cryptography voting protocol given its level security. Our protocol protects the confidentiality of voters because each voter encrypts their choice before casting their vote, offers public verifiability using a signing algorithm, the final result is correctly computed using homomorphic property, and works even in the presence of an adversary due to the propriety of non-malleability. Moreover, the protocol prevents some parties colluding to fix the vote results.

Keywords: Elgamal encryption, non-malleability, plaintext aware, e-voting

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

Procedia PDF Downloads 86