Search results for: input randomization

Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Belloui Bouzid

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In this paper, broadband design of erbium doped fiber amplifier (EDFA) is demonstrated and proved experimentally. High and broad gain is covered in C and L bands. The used technique combines, in one configuration, two double passes with split band structure for the amplification of two traveled signals one for the C band and the other for L band. This new topology is to investigate the trends of high gain and wide amplification at different status of pumping power, input wavelength, and input signal power. The presented paper is to explore the performance of EDFA gain using what it can be called double pass double branch wide band amplification configuration. The obtained results show high gain and wide broadening range of 44.24 dB and 80 nm amplification respectively.

Keywords: erbium doped fiber amplifier, erbium doped fiber laser, optical amplification, fiber laser

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Authors: Andre Slonopas, Zona Kostic, Warren Thompson

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Obfuscation is one of the most useful tools to prevent network compromise. Previous research focused on the obfuscation of the network communications between external-facing edge devices. This work proposes the use of two edge devices, external and internal facing, which communicate via private IPv4 addresses in a software-defined pseudo-random IP hopping. This methodology does not require additional IP addresses and/or resources to implement. Statistical analyses demonstrate that the hopping surface must be at least 1e3 IP addresses in size with a broad standard deviation to minimize the possibility of coincidence of monitored and communication IPs. The probability of breaking the hopping algorithm requires a collection of at least 1e6 samples, which for large hopping surfaces will take years to collect. The probability of dropped packets is controlled via memory buffers and the frequency of hops and can be reduced to levels acceptable for video streaming. This methodology provides an impenetrable layer of security ideal for information and supervisory control and data acquisition systems.

Keywords: moving target defense, cybersecurity, network security, hopping randomization, software defined network, network security theory

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Authors: Tetsiki Taniguchi, Yoshio Karasawa

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This paper describes a subarray based low computational design method of multiuser massive multiple input multiple output (MIMO) system. In our previous works, use of large array is assumed only in transmitter, but this study considers the case both of transmitter and receiver sides are equipped with large array antennas. For this aim, receive arrays are also divided into several subarrays, and the former proposed method is modified for the synthesis of a large array from subarrays in both ends. Through computer simulations, it is verified that the performance of the proposed method is degraded compared with the original approach, but it can achieve the improvement in the aspect of complexity, namely, significant reduction of the computational load to the practical level.

Keywords: large array, massive multiple input multiple output (MIMO), multiuser, singular value decomposition, subarray, zero forcing

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Authors: Wan-Hsin Hsieh, Chieh-Fu Chang, Ming-Seng Kao

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In this paper, a novel method termed the Phase Coherence Acquisition (PCA) is proposed for pseudo-random (PN) sequence acquisition. By employing complex phasors, the PCA requires only complex additions in the order of N, the length of the sequence, whereas the conventional method utilizing fast Fourier transform (FFT) requires complex multiplications and additions both in the order of Nlog2N . In order to combat noise, the input and local sequences are partitioned and mapped into complex phasors in PCA. The phase differences between pairs of input and local phasors are utilized for acquisition, and thus complex multiplications are avoided. For more noise-robustness capability, the multi-layer PCA is developed to extract the code phase step by step. The significant reduction of computational loads makes the PCA an attractive method, especially when the sequence length of is extremely large which becomes intractable for the FFT-based acquisition.

Keywords: FFT, PCA, PN sequence, convolution theory

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Hechmi Saidi, Noureddine Hamdi

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The performance of decode and forward (DF) multihop free space optical (FSO) scheme deploying multiple input multiple output (MIMO) configuration under gamma-gamma (GG) statistical distribution, that adopts M-ary pulse position modulation (MPPM) coding, is investigated. We have extracted exact and estimated values of symbol-error rates (SERs) respectively. The probability density function (PDF)’s closed-form formula is expressed for our designed system. Thanks to the use of DF multihop MIMO FSO configuration and MPPM signaling, atmospheric turbulence is combatted; hence the transmitted signal quality is improved.

Keywords: free space optical, gamma gamma channel, radio frequency, decode and forward, multiple-input multiple-output, M-ary pulse position modulation, symbol error rate

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Authors: Marlene Perez Villalpando, Kelly Joel Gurubel Tun

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In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances.

Keywords: hydropower, high order neural network, Kalman filter, optimal control

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Authors: Aishwarya Talapuru, Sri Silpa Padmanabhuni, B. Jyoshna

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Cryptography helps in preventing threats to information security by providing various algorithms. This study introduces a new symmetric key encryption algorithm for information security which is linked with the "raagas" which means Indian traditional scale and pattern of music notes. This algorithm takes the plain text as input and starts its encryption process. The algorithm then randomly selects a raaga from the list of raagas that is assumed to be present with both sender and the receiver. The plain text is associated with the thus selected raaga and an intermediate cipher-text is formed as the algorithm converts the plain text characters into other characters, depending upon the rules of the algorithm. This intermediate code or cipher text is arranged in various patterns in three different rounds of encryption performed. The total number of rounds in the algorithm is equal to the multiples of 3. To be more specific, the outcome or output of the sequence of first three rounds is again passed as the input to this sequence of rounds recursively, till the total number of rounds of encryption is performed. The raaga selected by the algorithm and the number of rounds performed will be specified at an arbitrary location in the key, in addition to important information regarding the rounds of encryption, embedded in the key which is known by the sender and interpreted only by the receiver, thereby making the algorithm hack proof. The key can be constructed of any number of bits without any restriction to the size. A software application is also developed to demonstrate this process of encryption, which dynamically takes the plain text as input and readily generates the cipher text as output. Therefore, this algorithm stands as one of the strongest tools for information security.

Keywords: cipher text, cryptography, plaintext, raaga

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Authors: Attaullah Khawaja, Amna Shabbir

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Channel inversion is one of the simplest techniques for multiuser downlink systems with single-antenna users. In this paper regularized channel inversion under equal power constraint in the multiuser multiple input multiple output (MU-MIMO) broadcast channels has been considered. Sum capacity with plain channel inversion also known as Zero Forcing Beam Forming (ZFBF) and optimum sum capacity using Dirty Paper Coding (DPC) has also been investigated. Analysis and simulations show that regularization enhances the system performance and empower linear growth in Sum Capacity and specially work well at low signal to noise ratio (SNRs) regime.

Keywords: broadcast channel, channel inversion, multiple antenna multiple-user wireless, multiple-input multiple-output (MIMO), regularization, dirty paper coding (DPC), sum capacity

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Authors: Shubhakar Gupta, Dhruv Prakash, Apoorv Mehta

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In this project, we design a modular robot capable of using and switching between multiple methods of propulsion and classifying terrain, based on an Inertial Measurement Unit (IMU) input. We wanted to make a robot that is not only intelligent in its functioning but also versatile in its physical design. The advantage of a modular robot is that it can be designed to hold several movement-apparatuses, such as wheels, legs for a hexapod or a quadpod setup, propellers for underwater locomotion, and any other solution that may be needed. The robot takes roughness input from a gyroscope and an accelerometer in the IMU, and based on the terrain classification from an artificial neural network; it decides which method of propulsion would best optimize its movement. This provides the bot with adaptability over a set of terrains, which means it can optimize its locomotion on a terrain based on its roughness. A feature like this would be a great asset to have in autonomous exploration or research drones.

Keywords: modular robotics, terrain detection, terrain classification, neural network

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Authors: Derick Shi-Chen Ou, Liang-Yu Chen

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Background: Acupuncture, a potential alternative, and complementary therapy revealed insufficient evidence in depression treatment. The efficacy of acupuncture treatment was still uncertainty. To evaluate the effect of acupuncture in treating depression, the randomized controlled trials (RCTs) were examined. Methods: RCTs of the acupuncture therapy in treating major depression were searched from MEDLINE from 2007 to 2017. Keywords used for searching strategy included acupuncture, acupoint, and major depressive disorder. Results: Among the nine RCTs, four studies demonstrated great improvement in acupuncture treatment and five studies revealed the effectiveness of acupuncture intervention in medication. General trends suggest that acupuncture treatment is as effective as antidepressants with minimal side effects. Conclusion: Despite the promising results from the RCTs, there are still a variety of limitations, including small sample size, imprecise enrollment criteria, difficulties with blinding, randomization, short duration of study and lack of longitudinal follow-up. Therefore, the evidence that acupuncture as an alternative therapy for depression is inconclusive. More rigorously designed RCTs should be conducted in the future.

Keywords: acupuncture, major depressive disorders, randomized clinical trials, antidepressants

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Authors: A. Karimzad Ghavidel, M. Zadshakouyan

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Laser cutting is a very common production method for cutting 2D polymeric parts. Developing of polymer composites with nano-fibers makes important their other properties like laser workability. The aim of this research is investigation of the influence different laser cutting conditions on the dimensional accuracy of parts and holes from poly methyl methacrylate (PMMA)/carbon nanotubes (CNTs) material. Experiments were carried out by considering of CNTs (in four level 0,0.5, 1 and 1.5% wt.%), laser power (60, 80, and 100 watt) and cutting speed 20, 30, and 40 mm/s as input variable factors. The results reveal that CNTs adding improves the laser workability of PMMA and the increasing of power has a significant effect on the part and hole size. The findings also show cutting speed is effective parameter on the size accuracy. Eventually, the statistical analysis of results was done, and calculated mathematical equations by the regression are presented for determining relation between input and output factor.

Keywords: dimensional accuracy, PMMA, CNTs, laser cutting

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Authors: Zahra Ahmadi, Fazlollah Adibnia

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Scheduling of tasks and the optimal allocation of resources in the cloud are based on the dynamic nature of tasks and the heterogeneity of resources. Applications that are based on the scientific workflow are among the most widely used applications in this field, which are characterized by high processing power and storage capacity. In order to increase their efficiency, it is necessary to plan the tasks properly and select the best virtual machine in the cloud. The goals of the system are effective factors in scheduling tasks and resource selection, which depend on various criteria such as time, cost, current workload and processing power. Multi-criteria decision-making methods are a good choice in this field. In this research, a new method of work planning and resource allocation in a heterogeneous environment based on the modified AHP algorithm is proposed. In this method, the scheduling of input tasks is based on two criteria of execution time and size. Resource allocation is also a combination of the AHP algorithm and the first-input method of the first client. Resource prioritization is done with the criteria of main memory size, processor speed and bandwidth. What is considered in this system to modify the AHP algorithm Linear Max-Min and Linear Max normalization methods are the best choice for the mentioned algorithm, which have a great impact on the ranking. The simulation results show a decrease in the average response time, return time and execution time of input tasks in the proposed method compared to similar methods (basic methods).

Keywords: hierarchical analytical process, work prioritization, normalization, heterogeneous resource allocation, scientific workflow

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Authors: Naser Ojaroudi Parchin, Haleh Jahanbakhsh Basherlou, Raed A. Abd-Alhameed, Peter S. Excell

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In this paper, a multiple-input/multiple-output (MIMO) antenna design with polarization and radiation pattern diversity is presented for future smartphones. The configuration of the design consists of four double-fed circular-ring antenna elements located at different edges of the printed circuit board (PCB) with an FR-4 substrate and overall dimension of 75×150 mm². The antenna elements are fed by 50-Ohm microstrip-lines and provide polarization and radiation pattern diversity function due to the orthogonal placement of their feed lines. A good impedance bandwidth (S₁₁ ≤ -10 dB) of 3.4-3.8 GHz has been obtained for the smartphone antenna array. However, for S₁₁ ≤ -6 dB, this value is 3.25-3.95 GHz. More than 3 dB realized gain and 80% total efficiency are achieved for the single-element radiator. The presented design not only provides the required radiation coverage but also generates the polarization diversity characteristic.

Keywords: cellular communications, multiple-input/multiple-output systems, mobile-phone antenna, polarization diversity

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Authors: Yansong Chen

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In the age of media convergence, new media technology is constantly impacting, changing, and even reshaping the limits of Art. From the technological ontology of the new media art, the concept of interaction design has always been dominated by I/O (Input/Output) systems through the ages, which ignores the content of systems and kills the aura of art. Light, as a fusion media, basically comes from the extension of some human feelings and can be the content of the input or the effect of output. In this paper, firstly, on the basis of literature review, the interaction characteristics research was conducted on light. Secondly, starting from discourse patterns of people and machines, people and people, people, and imagining things, we propose three light modes: object-oriented interaction, Immersion interaction, Tele-Presence interaction. Finally, this paper explains how to regain the aura of art through light elements in new media art and understand multiple levels of 'Interaction design'. In addition, the new media art, especially the light-based interaction art, enriches the language patterns and motivates emerging art forms to be more widespread and popular, which achieves its aesthetics growth.

Keywords: new media art, interaction design, light art, immersion

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Authors: Mary Rosa Espinosa-Ochoa

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Some constructions appear suddenly in children’s speech and are productive from the beginning. These constructions are supported by others, previously acquired, with which they share semantic and pragmatic features. Thus, for example, the acquisition of the passive voice in German is supported by other constructions with which it shares the lexical verb sein (“to be”). This also occurs in Spanish, in the acquisition of the progressive aspectual periphrasis estar (“to be”) + verb root + -ndo (present participle), supported by locative constructions acquired earlier with the same verb. The periphrasis shares with the locative constructions not only the lexical verb estar, but also pragmatic relations. Both constructions can be used to answer the question ¿Dónde está? (“Where is he/she/it?”), whose answer could be either Está aquí (“He/she/it is here”) or Se está bañando (“He/she/it is taking a bath”).This study is a corpus-based analysis of two children (1;08-2;08) and the input directed to them: it proposes that the pragmatic and semantic support from previously-acquired constructions comes from the input, during interaction with others. This hypothesis is based on analysis of constructions with estar, whose use to express temporal change (which differentiates it from its counterpart ser [“to be”]), is given in variation sets, similar to those described by Küntay and Slobin (2002), that allow the child to perceive the change of place experienced by nouns that function as its grammatical subject. For example, at different points during a bath, the mother says: El jabón está aquí “The soap is here” (beginning of bath); five minutes later, the soap has moved, and the mother says el jabón está ahí “the soap is there”; the soap moves again later on and she says: el jabón está abajo de ti “the soap is under you”. “The soap” is the grammatical subject of all of these utterances. The Spanish verb + -ndo is a progressive phase aspect encoder of a dynamic state that generates a token. The verb + -ndo is also combined with verb estar to encode. It is proposed here that the phases experienced in interaction with the adult, in events related to the verb estar, allow a child to generate this dynamicity and token reading of the verb + -ndo. In this way, children begin to produce the periphrasis suddenly and productively, even though neither the periphrasis nor the verb + -ndo itself are frequent in adult speech.

Keywords: child language acquisition, input, variation sets, Spanish language

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Siminfar Samakoush Galougah

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In this paper, we study two power allocation problems for an uplink user-centric (UC) cell-free massive multiple-input multiple-output (CF-mMIMO) system. Besides, we assume each access point (AP) is connected to a central processing unit (CPU) via a fronthaul link with limited capacity. To efficiently use the fronthaul capacity, two strategies for transmitting signals from APs to the CPU are employed, namely, compress-forward estimate (CFE), estimate-compress-forward (ECF). The capacity of the aforementioned strategies in user-centric CF-mMIMO is drived. Then, we solved the two power allocation problems with minimum Spectral Efficiency (SE) and sum-SE maximization objectives for ECF and CFE strategies.

Keywords: cell-free massive MIMO, limited capacity fronthaul, spectral efficiency

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Authors: Zhan Zheng-Qun, Li Min, Xie Yan

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Technology plays a key role in determining productivity and economy development in a country. The process of enterprises’ innovation can be seen as a process of knowledge management including the process of knowledge attainment; acquisition and converting and integrating into new knowledge. This research analyzes the influence factors and mechanism of the independent innovation of high-tech enterprises in the year 1995-2013. The result shows that the technology import has a significant positive effect on the innovation capacity of enterprises. And the absorptive capacity, represented by the research outlay input and research staff input, has a significant positive effect on the innovation capacity of enterprises. Furthermore, the effect of technology import on the independent research capacity of high-tech enterprises is significantly positively affected by their absorptive capacity.

Keywords: technology import, innovation capacity, absorptive capacity, high-tech industry

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Authors: Manvar Sagar, Nikul Virpariya

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The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Po-Hui Yang, Jing-Min Chen, Po-Yu Kuo, Chia-Chun Wu

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This paper proposed a high resolution Vernier Delay Line (VDL) measurement circuit with coarse and fine detection mechanism, which improved the trade-off problem between high resolution and less delay cells in traditional VDL circuits. And the measuring time of proposed measurement circuit is also under the high resolution requests. At first, the testing range of input signal which proposed high resolution delay line is detected by coarse detection VDL. Moreover, the delayed input signal is transmitted to fine detection VDL for measuring value with better accuracy. This paper is implemented at 0.18μm process, operating frequency is 100 MHz, and the resolution achieved 2.0 ps with only 16-stage delay cells. The test range is 170ps wide, and 17% stages saved compare with traditional single delay line circuit.

Keywords: vernier delay line, D-type flip-flop, DFF, metastable phenomenon

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Shih-Pin Chen, Shih-Syuan You

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This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria.

Keywords: dissemblance index, forecasting, fuzzy sets, linear regression

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Authors: Matthew F. Gibson, Narasimha D. Rao, Raphael B. Slade, Joana Portugal Pereira, Joeri Rogelj

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Globally, India’s population is among the most severely impacted by nutrient deficiency, yet millions of tonnes of food are lost before reaching consumers. Across food groups, grains represent the largest share of daily calories and overall losses by mass in India. If current losses remain unresolved and follow projected population rates, we estimate, by 2030, losses from grains for human consumption could increase by 1.3-1.8 million tonnes (Mt) per year against current levels of ~10 Mt per year. This study quantifies energy input to minimise storage losses across India, responsible for a quarter of grain supply chain losses. In doing so, we identify and explore a Sustainable Development Goal (SDG) triplet between SDG₂, SDG₇, and SDG₁₂ and provide insight for development of joined up agriculture and health policy in the country. Analyzing rice, wheat, maize, bajra, and sorghum, we quantify one route to reduce losses in supply chains, by modelling the energy input to maintain favorable climatic conditions in modern silo storage. We quantify key nutrients (calories, protein, zinc, iron, vitamin A) contained within these losses and calculate roughly how much deficiency in these dietary components could be reduced if grain losses were eliminated. Our modelling indicates, with appropriate uncertainty, maize has the highest energy input intensity for storage, at 110 kWh per tonne of grain (kWh/t), and wheat the lowest (72 kWh/t). This energy trade-off represents 8%-16% of the energy input required in grain production. We estimate if grain losses across the supply chain were saved and targeted to India’s nutritionally deficient population, average protein deficiency could reduce by 46%, calorie by 27%, zinc by 26%, and iron by 11%. This study offers insight for development of Indian agriculture, food, and health policy by first quantifying and then presenting benefits and trade-offs of tackling food grain losses.

Keywords: energy, food loss, grain storage, hunger, India, sustainable development goal, SDG

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Authors: Mario A. Maggioni, Domenico Rossignoli, Simona Beretta, Sara Balestri

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The paper presents the results of a Longitudinal Randomized Control Trial implemented in 2016 two State Prisons in California (USA). The subjects were randomly assigned to a 10-months program (GRIP, Guiding Rage Into Power) aiming at undoing the destructive behavioral patterns that lead to criminal actions by raising the individual’s 'mindfulness'. This study tests whether the participation to this program (treatment), based on strong relationships and mutual help, affects pro-social behavior of participants, in particular with reference to trust and inequality aversion. The research protocol entails the administration of two questionnaires including a set of behavioral situations ('games') - widely used in the relevant literature in the field - to 80 inmates, 42 treated (enrolled in the program) and 38 controls. The first questionnaire has been administered before treatment and randomization took place; the second questionnaire at the end of the program. The results of a Difference-in-Differences estimation procedure, show that trust significantly increases GRIP participants to compared to the control group. The result is robust to alternative estimation techniques and to the inclusion of a set of covariates to further control for idiosyncratic characteristics of the prisoners.

Keywords: behavioral economics, difference in differences, longitudinal study, pro-social preferences

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Authors: Dao Phuong Nam, Tran Van Tuyen, Do Trong Tan, Bui Minh Dinh, Nguyen Van Huong

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In this paper, we investigate the adaptive optimal control law for continuous-time systems with input disturbances and unknown parameters. This paper extends previous works to obtain the robust control law of uncertain systems. Through theoretical analysis, an adaptive dynamic programming (ADP) based optimal control is proposed to stabilize the closed-loop system and ensure the convergence properties of proposed iterative algorithm. Moreover, the global asymptotic stability (GAS) for closed system is also analyzed. The theoretical analysis for continuous-time systems and simulation results demonstrate the performance of the proposed algorithm for an inverted pendulum system.

Keywords: approximate/adaptive dynamic programming, ADP, adaptive optimal control law, input state stability, ISS, inverted pendulum

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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