A study of maintenance policies for second-hand products

This paper proposes two maintenance schemes for reused products. In order to reduce the purchasing cost or conform to the concept of environmental protection, reuse is considered as one of the most efficient strategies. However, the initial quality of reused product is often inferior to the new one and then product failures will occur frequently during usage period. Therefore, two periodical preventive maintenance (PM) policies in this paper are developed to decrease the high failure rate of the second-hand products. When a second-hand product with Weibull life time distribution of known age is intended to be used for a pre-specified period of time, the optimal number of PM actions and the corresponding maintenance degree are derived such that the expected total maintenance cost is minimized. The impact of providing preventive maintenance is illustrated through numerical examples.     

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

Improving job scheduling performance with parallel access to replicas in Data Grid environment

Data Grid has evolved to be the solution for data-intensive applications, such as High Energy Physics (HEP), astrophysics, and computational genomics. These applications usually have large input of data to be analyzed and these input data are widely replicated across Data Grid to improve the performance. The job scheduling performance on traditional computing jobs can be studied using queuing theory. However, with the addition of data transfer, the job scheduling performance is too complex to be modeled. In this research, we study the impact of data transfer on the performance of job scheduling in the Data Grid environment. We have proposed a parallel downloading system that supports replicating data fragments and parallel downloading of replicated data fragments, to improve the job scheduling performance. The performance of the parallel downloading system is compared with non-parallel downloading system, using three scheduling heuristics: Shortest Turnaround Time (STT), Least Relative Load (LRL) and Data Present (DP). Our simulation results show that the proposed parallel download approach greatly improves the Data Grid performance for all three scheduling algorithms, in terms of the geometric mean of job turnaround time. The advantage of parallel downloading system is most evident when the Data Grid has relatively low network bandwidth and relatively high computing power.     

A novel design of variable-rate RS encoder for ubiquitous high performance multimedia service in Gbps transmission system

In this paper, we have first reviewed the state-of-the-art technologies related to DOCSIS 3.0 high-speed data transmission system and also looked at current trends in the next generation Gbps transmission technology, which is one of key technology for ubiquitous high performance multimedia service environments. Also, we implemented the RS encoder which was designed using VHDL and verified its operation in order to confirm our design through the ModelSim simulation analysis tool.     

Genetic algorithm-based heuristics for an open shop scheduling problem with setup,processing, and removal times separated

This report proposes a solution to the open shop scheduling problem with the objective of minimizing total job tardiness in the system. Some practical processing restrictions, such as independent setup and dependent removal times, are taken into account as well. The addressed problem is first described as a 0–1 integer programming model, and is then solved optimally. Subsequently, some hybrid genetic-based heuristics are proposed to solve the problem in an acceptable computation time. To demonstrate the adaptability of these heuristics, some performance comparisons are made with solutions provided by running either a mathematical programming model or certain classic meta-heuristics such as genetic algorithm, simulated annealing, and tabu search in various manufacturing scenarios. The experimental results show that the hybrid genetic-based heuristics perform well, especially the DGA. However, these heuristics require some more additional computations but are still acceptable.     

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

In this paper, the problem of outsourcing the selective encryption of a medical image to cloud by resource-constrained devices such as smart phone is addressed, without revealing the cover image to cloud using steganography. In the proposed framework, the region of interest of the medical image is first detected using a visual saliency model. The detected important data is then embedded in a host image, producing a stego image which is outsourced to cloud for encryption. The cloud which has powerful resources, encrypts the image and sent back the encrypted marked image to the client. The client can then extract the selectively encrypted region of interest and can combine it with the region of non-interest to form a selectively encrypted image, which can be sent to medical specialists and healthcare centers. Experimental results and analysis validate the effectiveness of the proposed framework in terms of security, image quality, and computational complexity and verify its applicability in remote patient monitoring centers.     

Enhanced sample concentration on a three-dimensional origami paper-based analytical device with non-uniform assay channel

Traditional microfluidic paper-based analytical devices (μPADs) consist of a flat straight channel printed on a paper substrate. Such devices provide a promising low-cost solution for a variety of biomedical assays. However, they have a relatively high sample consumption due to their use of external reservoirs. Moreover, in μPADs based on the ion concentration polarization (ICP) effect, controlling the cross-sectional area of the Nafion membrane relative to that of the hydrophilic channel is difficult. Accordingly, the present study utilizes an origami technique to create a μPAD with a three-dimensional (3D) structure. The μPAD features short channels and embedded reservoirs, and therefore reduces both the driving voltage requirement and the sample consumption. Moreover, the preconcentration effect is enhanced through the use of an additional hydrophilic area adjacent to the Nafion membrane. The existence of electroosmotic flow (EOF) within the proposed device is confirmed using a current-monitoring method. In addition, the occurrence of ICP is evaluated by measuring the current–voltage response of the device at external voltages ranging from 0 to 50 V. The experimental results obtained for a fluorescein sample with an initial concentration of 10^?5 M show that a 100-fold enhancement factor can be achieved given the use of a non-uniform-geometry design for the assay channel and an additional hydrophilic region with an area equal to approximately 10% of the channel cross-sectional area. Finally, a 100-fold factor can also be achieved for a fluorescein isothiocyanate sample with an initial concentration of 10^?6 M given an external driving voltage of 40 V.     

Securing against brute-force attack: A hash-based RFID mutual authentication protocol using a secret value

Radio Frequency IDentification (RFID ) system is a contactless automatic identification system using small, low-cost RFID tags. It enables recognition of the tag information via radio frequency communication, by attaching an RFID tag to an animate or inanimate object. Since an RFID system has the advantage of simultaneously recognizing massive amounts of information, it is expected to replace the bar-code system. The most important problem with an RFID system is that an adversary can access the tag information, which gives rise to privacy and forgery problems. This paper presents a hash-based mutual authentication protocol as a solution. The proposed protocol is designed to send a random number generated by a tag to a back-end server without disclosure. Moreover it substitutes a random number with a secret value, which is employed in a response message. The properties of the proposed protocol enable constant creation of distinct response messages without interferences from intended or meaningless requests generated by an adversary, while the secret value is not directly transmitted. Our proposed protocol make is difficult for an attacker to launch successful brute-force attacks against our approach.     

Molecular Verification of Rule-Based Systems Based on DNA Computation

Various graphic techniques have been developed to analyze structural errors in rule-based systems that utilize inference (propositional) logic rules. Four typical errors in rule-based systems are: redundancy (numerous rule sets resulting in the same conclusion); circularity (a rule leading back to itself); incompleteness (deadends or a rule set conclusion leading to unreachable goals); and inconsistency (rules conflicting with each other). This study presents a new DNA-based computing algorithm mainly based upon Adleman's DNA operations. It can be used to detect such errors. There are three phases to this molecular solution: rule-to-DNA transformation design, solution space generation, and rule verification. We first encode individual rules using relatively short DNA strands, and then generate all possible rule paths by the directed joining of such short strands to form longer strands. We then conduct the verification algorithm to detect errors. The potential of applying this proposed DNA computation algorithm to rule verification is promising given the operational time complexity of O(n*q), in which n denotes the number of fact clauses in the rule base and q is the number of rules with longest inference chain.