Search results for: Feed Forward Neural Networks

Authors: Lin Cheng, Zijiang Yang

Abstract:

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN.

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Authors: Musa'ed N. Almarshad, Saleh A. Alshebeili, Mourad Barkat

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A challenging problem in radar signal processing is to achieve reliable target detection in the presence of interferences. In this paper, we propose a novel algorithm for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithm, termed the forward automatic censored cell averaging detector (F-ACCAD), consists of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The F-ACCAD algorithm does not require any prior information about the clutter parameters nor does it require the number of interfering targets. The effectiveness of the F-ACCAD algorithm is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.

Keywords: CFAR, Log-normal clutter, Censoring, Probabilityof detection, Probability of false alarm, Probability of falsecensoring.

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Authors: Young-Seok Choi

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It has been known that a characteristic Burst-Suppression (BS) pattern appears in EEG during the early recovery period following Cardiac Arrest (CA). Here, to explore the relationship between cortical and subcortical neural activities underlying BS, extracellular activity in the parietal cortex and the centromedian nucleus of the thalamus and extradural EEG were recorded in a rodent CA model. During the BS, the cortical firing rate is extraordinarily high, and that bursts in EEG correlate to dense spikes in cortical neurons. Newly observed phenomena are that 1) thalamic activity reemerges earlier than cortical activity following CA, and 2) the correlation coefficient of cortical and thalamic activities rises during BS period. These results would help elucidate the underlying mechanism of brain recovery after CA injury.

Keywords: Cortex, thalamus, cardiac arrest, burst-suppression.

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Authors: C. Rajan, K. Geetha, C. Rasi Priya, S. Geetha

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Nature is the immense gifted source for solving complex problems. It always helps to find the optimal solution to solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide research area of networks which has set of independent nodes. The characteristics involved in MANET’s are Dynamic, does not depend on any fixed infrastructure or centralized networks, High mobility. The Bio-Inspired algorithms are mimics the nature for solving optimization problems opening a new era in MANET. The typical Swarm Intelligence (SI) algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf Search Algorithm (WSA) and so on. This work mainly concentrated on nature of MANET and behavior of nodes. Also it analyses various performance metrics such as throughput, QoS and End-to-End delay etc.

Keywords: Ant Colony Algorithm, Artificial Bee Colony algorithm, Bio-Inspired algorithm, Modified Termite Algorithm.

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Authors: Samir K. Deshmukh, Mayur M. Tajane

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In this work Membrane Distillation is applied to concentrate orange Juice. Clarified orange juice (11o Brix) obtained from fresh fruits and a sugar solution was subjected to membrane distillation. The experiments were performed on a flat sheet module using orange juice and sucrose solution as feeds. The concentration of a sucrose solution, used as a model fruit juice and also orange juice, was carried out in a direct contact membrane distillation using hydrophobic PTFE membrane of pore size 0.2 μm and porosity 70%. Surface modification of PTFE membrane has been carried out by treating membrane with alcohol and water solution to make it hydrophilic and then hydrophobicity was regained by drying. The influences of the feed temperature, feed concentration, flow rate, operating time on the permeate flux were studied for treated and non treated membrane. In this work treated and non treated membrane were compared in terms of water flux, Within the tested range, MD with surface modified membrane the water flux has been significantly improved by treating the membrane surface.

Keywords: Membrane Distillation, Surface Modification, Orange Juice. Polytetrafluoroethylene.

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Authors: Michael A. Folcik, Bijan Karimi

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A robot simulator was developed to measure and investigate the performance of a robot navigation system based on the relative position of the robot with respect to random obstacles in any two dimensional environment. The presented simulator focuses on investigating the ability of a fuzzy-neural system for object avoidance. A navigation algorithm is proposed and used to allow random navigation of a robot among obstacles when the robot faces an obstacle in the environment. The main features of this simulator can be used for evaluating the performance of any system that can provide the position of the robot with respect to obstacles in the environment. This allows a robot developer to investigate and analyze the performance of a robot without implementing the physical robot.

Keywords: Applications of Fuzzy Logic and Neural Networksin Robotics, Artificial Intelligence, Embedded Systems, MobileRobots, Robot Navigation, Robotics.

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Authors: Renzo Carta, Mario Cruccu, Francesco Desogus

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This work presents the experimental results obtained at a pilot plant which works with a slow, wet and catalytic pyrolysis process of dry fowl manure. This kind of process mainly consists in the cracking of the organic matrix and in the following reaction of carbon with water, which is either already contained in the organic feed or added, to produce carbon monoxide and hydrogen. Reactions are conducted in a rotating reactor maintained at a temperature of 500°C; the required amount of water is about 30% of the dry organic feed. This operation yields a gas containing about 59% (on a volume basis) of hydrogen, 17% of carbon monoxide and other products such as light hydrocarbons (methane, ethane, propane) and carbon monoxide in lesser amounts. The gas coming from the reactor can be used to produce not only electricity, through internal combustion engines, but also heat, through direct combustion in industrial boilers. Furthermore, as the produced gas is devoid of both solid particles and pollutant species (such as dioxins and furans), the process (in this case applied to fowl manure) can be considered as an optimal way for the disposal and the contemporary energetic valorization of organic materials, in such a way that is not damaging to the environment.

Keywords: Brushwood, fowl manure, kenaf, pilot plant, pyrolysis, pyrolysis gas.

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

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In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are class balancing, data shuffling, and standardization, were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the sequential model and ReLU activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: Spectroscopy, soluble solid content, pineapple, neural network.

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Authors: John O. Jacob, Abdullahi Mann, Olanrewaju I. Adeshina, Mohammed M. Ndamitso

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Strychnos spinosa, Detarium microcarpum, Diospyros mespiliformis, Dialium guineese and Gardenia ternifolia are some of the wild fruits consume in the villages around Minna, Niger State. This investigation was conducted to assess the nutritional potentials of these fruits both for human consumption and for possible application in animal feed formulations. Standard analytical methods were employed in the determination of the various nutritional parameters. The proximate analysis results showed that the moisture contents ranged between (6.17-10.70%); crude fat (2.04-8.85%); crude protein (5.16-6.80%); crude fibre (7.23-19.65%); Ash (3.46-5.56%); carbohydrate (57.77-69.79%); energy value (284.49-407 kcal/mg); Vitamin C (7.2-39.93 mg/100g). The mineral analysis shows that the selected wild fruits could contribute considerable amount of both micro and macro elements to human nutrition potassium, sodium and calcium range between; potassium (343.27-764.71%); sodium (155.04-348.44%); calcium (52.47-101%). The macro element for the fruits pulp were in the order K>Na>Mg>Ca, hence, they could be included in diet to supplement daily nutrient requirement and in animal feed formulations. The domestication of these fruits is also encouraged.

Keywords: Minerals, nutrition, supplements, wild fruits.

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Authors: Chontira Boonfung, Panarat Rattanaphanee

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Ethanol has become more attractive in fuel industry either as fuel itself or an additive that helps enhancing the octane number and combustibility of gasoline. This research studied a pressure swing adsorption using cassava-based adsorbent prepared from mixture of cassava starch and cassava pulp for dehydration of ethanol vapor. The apparatus used in the experiments consisted of double adsorption columns, an evaporator, and a vacuum pump. The feed solution contained 90-92 %wt of ethanol. Three process variables: adsorption temperatures (110, 120 and 130°C), adsorption pressures (1 and 2 bar gauge) and feed vapor flow rate (25, 50 and 75 % valve opening of the evaporator) were investigated. According to the experimental results, the optimal operating condition for this system was found to be at 2 bar gauge for adsorption pressure, 120°C for adsorption temperature and 25% valve opening of the evaporator. Production of 1.48 grams of ethanol with concentration higher than 99.5 wt% per gram of adsorbent was obtained. PSA with cassavabased adsorbent reported in this study could be an alternative method for production of nearly anhydrous ethanol. Dehydration of ethanol vapor achieved in this study is due to an interaction between free hydroxyl group on the glucose units of the starch and the water molecules.

Keywords: Adsorption, PSA, Ethanol, Dehydration, Cassava.

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: Transportation networks, freight delivery, data flow, monitoring, e-services.

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Authors: U.E.Mahrous, A. Abd El-Aziz, A.I.El-Shiekh, S.Z. EL-kholya

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Twenty four New Zealand white rabbits (12 does and 12 bucks) and twenty four Flanders (12 does and 12 bucks) rabbits, allotted into two feeding regime (6 for each breed, 3 males and 3 females) first one fed commercial ration and second one fed commercial diet plus sodium butyrate (300 g/ton). The obtained results showed that at end of 8th week experimental period New Zealand white rabbits were heavier body weight than Flanders rabbits (1934.55+39.05 vs. 1802.5+30.99 g); significantly high body weight gain during experimental period especially during 8th week (136.1+3.5 vs. 126.8+1.8 g/week); better feed conversion ratio during all weeks of experiment from first week (3.07+0.16 vs. 3.12+0.10) till the 8th week of experiment (5.54+0.16 vs. 5.76+0.07) with significantly high dressing percentages (0.54+0.01 vs. 0.52+0.01). Also all carcass cuts were significantly high in New Zealand white rabbits than Flanders. Females rabbits (at the same age) were lower body weight than males from start of experiment (941.1+39.8 vs.972.1+33.5 g) till the end of experiment (1833.64+37.69 vs. 1903.41+36.93 g); gained less during all weeks of experiment except during 8th week (132.1+2.3 vs. 130.9+3.4 g/week), with lower dressing percentage (0.52+0.01 vs. 0.53+0.01) and lighter carcass cuts than males, however, they had better feed conversion ratio during 1st week, 7th week and 8th week of experiment. Addition of 300g sodium butyrate/ton of rabbit increased the body weight of rabbits at the end of experimental period (1882.71+26.45 vs. 1851.5+49.82 g); improve body weight gain at 3rd, 4th, 5th, 6th and 7th week of experiment and significantly improve feed conversion ratio during all weeks of the experiment from 1st week (2.85+0.07 vs. 3.30+0.15) till the 8th week of the experiment (5.51+0.12 vs. 5.77+0.12). Also the dressing percentage was higher in Sodium butyrate fed groups than control one (0.53+0.01 vs. 0.52+0.01) and the most important results of feeding sodium butyrate is the reducing of the mortality percentage in rabbits during 8 week experiment to zero percentage as compared with 16% in control group.

Keywords: rabbit, productive performance, carcass quality, sodium byturate

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Authors: Reham A. El-mayet, Hesham M. El-Badawy, Salwa H. Elramly

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New generation mobile communication networks have the ability of supporting triple play. In order that, Orthogonal Frequency Division Multiplexing (OFDM) access techniques have been chosen to enlarge the system ability for high data rates networks. Many of cross-layer modeling and optimization schemes for Quality of Service (QoS) and capacity of downlink multiuser OFDM system were proposed. In this paper, the Maximum Weighted Capacity (MWC) based resource allocation at the Physical (PHY) layer is used. This resource allocation scheme provides a much better QoS than the previous resource allocation schemes, while maintaining the highest or nearly highest capacity and costing similar complexity. In addition, the Delay Satisfaction (DS) scheduling at the Medium Access Control (MAC) layer, which allows more than one connection to be served in each slot is used. This scheduling technique is more efficient than conventional scheduling to investigate both of the number of users as well as the number of subcarriers against system capacity. The system will be optimized for different operational environments: the outdoor deployment scenarios as well as the indoor deployment scenarios are investigated and also for different channel models. In addition, effective capacity approach [1] is used not only for providing QoS for different mobile users, but also to increase the total wireless network's throughput.

Keywords: Cross-layer, effective capacity, LTE, OFDM, QoS, resource allocation, wireless networks.

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Authors: Wen-Yuh Jywe, Bor-Jeng Lin, Jing-Chung Shen, Jeng-Dao Lee, Hsueh-Liang Huang, Ming-Chen Cho

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The dynamical contouring error is a critical element for the accuracy of machine tools. The contouring error is defined as the difference between the processing actual path and commanded path, which is implemented by following the command curves from feeding driving system in machine tools. The contouring error is resulted from various factors, such as the external loads, friction, inertia moment, feed rate, speed control, servo control, and etc. Thus, the study proposes a 2D compensating system for the contouring accuracy of machine tools. Optical method is adopted by using stable frequency laser diode and the high precision position sensor detector (PSD) to performno-contact measurement. Results show the related accuracy of position sensor detector (PSD) of 2D contouring accuracy compensating system was ±1.5 μm for a calculated range of ±3 mm, and improvement accuracy is over 80% at high-speed feed rate.

Keywords: Position sensor detector, laser diode, contouring accuracy, machine tool.

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Authors: Chien-Min Chu, Bor-Wen Tsai, Kang-Tsung Chang

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Landslide susceptibility map delineates the potential zones for landslide occurrence. Previous works have applied multivariate methods and neural networks for mapping landslide susceptibility. This study proposed a new approach to integrate decision tree model and spatial cluster statistic for assessing landslide susceptibility spatially. A total of 2057 landslide cells were digitized for developing the landslide decision tree model. The relationships of landslides and instability factors were explicitly represented by using tree graphs in the model. The local Getis-Ord statistics were used to cluster cells with high landslide probability. The analytic result from the local Getis-Ord statistics was classed to create a map of landslide susceptibility zones. The map was validated using new landslide data with 482 cells. Results of validation show an accuracy rate of 86.1% in predicting new landslide occurrence. This indicates that the proposed approach is useful for improving landslide susceptibility mapping.

Keywords: Landslide susceptibility Zonation, Decision treemodel, Spatial cluster, Local Getis-Ord statistics.

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non_real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: I. Falconett, K. Nagasaka

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This study investigates the performance of radial basis function networks (RBFN) in forecasting the monthly CO2 emissions of an electric power utility. We also propose a method for input variable selection. This method is based on identifying the general relationships between groups of input candidates and the output. The effect that each input has on the forecasting error is examined by removing all inputs except the variable to be investigated from its group, calculating the networks parameter and performing the forecast. Finally, the new forecasting error is compared with the reference model. Eight input variables were identified as the most relevant, which is significantly less than our reference model with 30 input variables. The simulation results demonstrate that the model with the 8 inputs selected using the method introduced in this study performs as accurate as the reference model, while also being the most parsimonious.

Keywords: Correlation analysis, CO2 emissions forecasting, electric power utility, radial basis function networks.

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Authors: B. Vahdani, R. Tavakkoli-Moghaddam, A. Baboli, S. M. Mousavi

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Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.

Keywords: Location-allocation model, recycling collection networks, fuzzy mathematical programming.

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Authors: Salah Gariani, Islam Shyha, Osama Elgadi, Khaled Jegandi

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In this work, a cooling coherent round nozzle was developed and the impact of nozzle placement (i.e. nozzle angle and stand-off/impinging distance) on the machinability of Ti-6Al-4V was evaluated. Key process measures were cutting force, workpiece temperature, tool wear, burr formation and average surface roughness (Ra). Experimental results showed that nozzle position at a 15° angle in the feed direction and 45°/60° against feed direction assisted in minimising workpiece temperature. A stand-off distance of 55 and 75 mm is also necessary to control burr formation, workpiece temperature and Ra, but coherent nozzle orientation has no statistically significant impact on the mean values of cutting force and tool wear. It can be concluded that stand-off distance is more substantially significant than nozzle angles when step shoulder milling Ti-6Al- 4V using vegetable oil-based cutting fluid.

Keywords: Coherent round nozzle, step shoulder milling, Ti-6Al-4V, vegetable oil-based cutting fluid.

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Authors: Hamada A. Ahmed, Kadry M. Sadek, Ayman E. Taha

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The effects of basil and/or chamomile seed supplementation on the growth of Hubbard broiler chicks were evaluated. The antioxidant effects of these supplements were also assessed. 120 1-day-old broiler chicks were randomly divided into four equal groups. The control group (group 1) was fed a basal diet (BD) without supplementation. Groups 2, 3, and 4 were fed the BD supplemented with 10g basil, 10g chamomile, and 5g basil plus 5g chamomile per kg of food, respectively. Basil supplementation alone or in combination with chamomile non-significantly (P≥0.05) increased final body weight (3.2% and 0.3%, respectively) and weight gain (3.5% and 3.6%, respectively) over the experimental period. Chamomile supplementation alone non-significantly (P≥0.05) reduced final body weight and weight gain over the experimental period by 1.7% and 1.7%, respectively. In comparison to the control group, herbal seed supplementation reduced feed intake and improved the feed conversion and protein efficiency ratios. In general, basil seed supplementation stimulated chicken growth and improved the feed efficiency more effectively than chamomile seed supplementation. The antioxidant activities of basil and/or chamomile supplementation were examined in the thymus, bursa, and spleen. In chickens that received supplements, the level of malondialdehyde was significantly decreased, whereas the activities of glutathione, superoxide dismutase, and catalase were significantly increased (P<0.05). Supplementation of basil and/or chamomile did not affect blood protein levels, but had lipid-lowering effects as evidenced by reduced serum levels of total lipids, triglycerides, and cholesterol. In conclusion, supplementation of basil and/or chamomile improved growth parameters in broiler chicks and had antioxidant and blood lipid-lowering effects. These beneficial effects of basil and/or chamomile supplementation resulted in economically viable production of high-quality white meat containing no harmful residues.

Keywords: Herbal additives, basil, chamomile, broiler, growth performance, antioxidant.

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Authors: J. Olamaei , T. Niknam, M. Nayeripour

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This paper presents an approach for daily optimal operation of distribution networks considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. A genetic algorithm is used to solve the optimal operation problem. The approach is tested on an IEEE34 buses distribution feeder.

Keywords: Distributed Generator, Daily Optimal Operation, Genetic Algorithm.

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Authors: Kamarularifin Abd Jalil, John Dunlop

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A Personal Distributed Environment (PDE) is an example of an IP-based system architecture designed for future mobile communications. In a single PDE, there exist several Subnetworks hosting devices located across the infrastructure, which will inter-work with one another through the coordination of a Device Management Entity (DME). Some of these Sub-networks are fixed and some are mobile. In order to support Mobile Sub-networks mobility in the PDE, the PDE-NEMO protocol was proposed. This paper discussed the signalling cost analysis of PDE-NEMO by use of a detailed simulation model. The paper started with the introduction of the protocol, followed by the experiments and results and then followed by discussions.

Keywords: Mobile Network, PDE-NEMO, Signallling Cost.

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.

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Authors: Muhammad Rana, Quazi Mamun, Rafiqul Islam

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In the Internet of Things (IoT), many devices are connected and accumulate a sheer amount of data. These Internet-driven raw data need to be transferred securely to the end-users via dependable networks. Consequently, the challenges of IoT security in various IoT domains are paramount. Cryptography is being applied to secure the networks for authentication, confidentiality, data integrity and access control. However, due to the resource constraint properties of IoT devices, the conventional cipher may not be suitable in all IoT networks. This paper designs a robust and effective lightweight cipher to secure the IoT environment and meet the resource-constrained nature of IoT devices. We also propose a symmetric and block-cipher based lightweight cryptographic algorithm. The proposed algorithm increases the complexity of the block cipher, maintaining the lowest computational requirements possible. The proposed algorithm efficiently constructs the key register updating technique, reduces the number of encryption rounds, and adds a layer between the encryption and decryption processes.

Keywords: Internet of Things, IoT, cryptography block cipher, s-box, key management, IoT security.

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Authors: Frank Emmert-Streib, Matthias Dehmer

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Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Keywords: Dynamic Bayesian networks, microarray data, structure learning, Markov chain Monte Carlo.

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Authors: Lihui Ma, Kunlun Xin, Suiqing Liu

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Modern managements of water distribution system (WDS) need water quality models that are able to accurately predict the dynamics of water quality variations within the distribution system environment. Before water quality models can be applied to solve system problems, they should be calibrated. Although former researchers use GA solver to calibrate relative parameters, it is difficult to apply on the large-scale or medium-scale real system for long computational time. In this paper a new method is designed which combines both macro and detailed model to optimize the water quality parameters. This new combinational algorithm uses radial basis function (RBF) metamodeling as a surrogate to be optimized for the purpose of decreasing the times of time-consuming water quality simulation and can realize rapidly the calibration of pipe wall reaction coefficients of chlorine model of large-scaled WDS. After two cases study this method is testified to be more efficient and promising, and deserve to generalize in the future.

Keywords: Metamodeling, model calibration, radial basisfunction, water distribution system, water quality model.

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Authors: Efendi Zaenudin, Chien-Hung Huang, Ka-Lok Ng

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Essential genes play an important role in the survival of an organism. It has been shown that cancer-associated essential genes are genes necessary for cancer cell proliferation, where these genes are potential therapeutic targets. Also, it was demonstrated that mutations of the cancer-associated essential genes give rise to the resistance of immunotherapy for patients with tumors. In the present study, we focus on studying the biological effects of the essential genes from a network perspective. We hypothesize that one can analyze a biological molecular network by decomposing it into both three-node and four-node digraphs (subgraphs). These network subgraphs encode the regulatory interaction information among the network’s genetic elements. In this study, the frequency of occurrence of the subgraph-associated essential genes in a molecular network was quantified by using the statistical parameter, odds ratio. Biological effects of subgraph-associated essential genes are discussed. In summary, the subgraph approach provides a systematic method for analyzing molecular networks and it can capture useful biological information for biomedical research.

Keywords: Biological molecular networks, essential genes, graph theory, network subgraphs.

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Authors: Chad Goldsworthy, B. Rajeswari Matam

Abstract:

The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: Convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation.

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Authors: Jan Rychtar, Brian Stadler

Abstract:

We study how the outcome of evolutionary dynamics on graphs depends on a randomness on the graph structure. We gradually change the underlying graph from completely regular (e.g. a square lattice) to completely random. We find that the fixation probability increases as the randomness increases; nevertheless, the increase is not significant and thus the fixation probability could be estimated by the known formulas for underlying regular graphs.

Keywords: evolutionary dynamics, small-world networks

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Authors: Changyi Liu, Gui Wang, Matthew Dargusch

Abstract:

Helical milling operations are used to generate or enlarge boreholes by means of a milling tool. The bore diameter can be adjusted through the diameter of the helical path. The kinematics of helical milling on a three axis machine tool is analysed firstly. The relationships between processing parameters, cutting tool geometry characters with machined hole feature are formulated. The feed motion of the cutting tool has been decomposed to plane circular feed and axial linear motion. In this paper, the time varying cutting forces acted on the side cutting edges and end cutting edges of the flat end cylinder miller is analysed using a discrete method separately. These two components then are combined to produce the cutting force model considering the complicated interaction between the cutters and workpiece. The time varying cutting force model describes the instantaneous cutting force during processing. This model could be used to predict cutting force, calculate statics deflection of cutter and workpiece, and also could be the foundation of dynamics model and predicting chatter limitation of the helical milling operations.

Keywords: Helical milling, Hole machining, Cutting force, Analytical model, Time domain

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